# Vibe Coding with Cursor IDE ## Introduction & Preface *Figure: Cursor’s homepage branding itself as “The AI Code Editor,” reflecting its focus on leveraging AI for productivity.* Welcome to *Vibe Coding with Cursor IDE*! This e-book introduces *vibe coding*, a new programming philosophy emphasizing creativity, flow, and intuition, and explores how the Cursor IDE embodies this approach. We’ll define what vibe coding means and how Cursor – a modern AI-integrated code editor – enables developers to “code at the speed of thought.” Our target audience is beginner to intermediate de ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=Image%3A%20Logan%20Kilpatrick))ous about AI-assisted coding. Whether you’re just starting out or already comfortable with traditional tools, our goal is to help you tap into a more intuitive and productive coding workflow. By the end of this e-book, you should understand the vibe coding mindset, know the history and features of Cursor, and be ready to build projects in a collaborative dance with AI. **Who this book is for:** Anyone excited about blending human creativity with AI power in software development. If you’ve found yourself frustrated by boilerplate code or wondering how to maintain *flow* state while coding, v ([Cursor vs GitHub Copilot - Which One Is Better for Engineers?](https://zencoder.ai/blog/cursor-vs-copilot#:~:text=Pros%3A))ht be the paradigm shift you need. We assume you have basic programming knowledge, but no expertise in AI is required – we’ll walk through everything step by step. **Learning goals:** After a brief history of vibe coding’s emergence and Cursor’s evolution, we’ll dive into practical guidance. You’ll learn how to set up Cursor IDE, use its AI-driven features, and adopt best practices for a *“vibey”* workflow. Hands-on tutorials and examples will show you how to collaborate with Cursor’s AI on real tasks – from generating code and debugging, to integrating external tools via Cursor’s plugin-like Model Context Protocol (MCP). Along the way, we’ll share quotes and insights from developers who ([Behind Cursor’s Success: Two PMFs | by Gru.ai | Medium](https://medium.com/@connect_33559/behind-cursors-success-two-pmfs-524051f2aa35#:~:text=1))ced this approach, and compare it with other coding philosophies and IDEs. By the conclusion, you’ll be equipped and inspired to start your own journey of vibe coding with Cursor, supported by resources for further learning. So, get comfortable and maybe put on your favorite coding music – let’s set the vibe and begin our exploration of *Vibe Coding with Cursor IDE*. ## 2. The Emergence of Vibe Coding Vibe coding has burst onto the tech scene as a new way of thinking about programming in the age of advanced AI assistants. The term **“vibe coding”** was famously coined only recently – in early 2025 – by AI luminaries like Andrej Karpathy to describe a style of coding that “is all about surrendering to the flow” rather than obsessing over low-level control. In other words, instead of meticulously planning and typing out every line, a developer in vibe coding mode collaborates with AI, riding a creative *flow state* where the AI handles the detailed implementation. As Karpathy quipped on social media, with sufficiently powerful AI models, one can *“give in to the vibes, embrace exponentials and forget that the code even exists”*. What was initially half-jokingly described has quickly caught on as a serious concept in developer communities. At its core, **vibe coding** means approaching software development more like an interactive jam session with your AI partner than a rigid engineering project. A key shift is in mindset: the programmer focuses on high-level ideas, intuition, and experimentation, while trusting the AI to fill in gaps and handle routine code generation. As one IBM technologist describes, *“if you’re out of ideas, or if you’re feeling lazy, just let Copilot (the AI) do it for you a little bit and see where it leads”*. Instead of strictly controlling every detail, the developer guides the AI, evaluates its output, and steers the project in a dialog. Ars Technica summed it up nicely: *“Instead of being about control and precision, vibe coding is all about surrendering to the flow, asking AI to write code without ever understanding how that code works”*. That latter clause hints at controversy – is it okay to accept code we don’t fully understand? We’ll discuss best practices later, but advocates argue that freeing oneself from minutiae lets creativity and rapid prototyping flourish. Culturally, vibe coding arose from the convergence of AI advancements and developer experimentation. Large language models (LLMs) like OpenAI’s GPT-4 and Anthropic’s Claude demonstrated in 2023–2024 that they could generate substantial, working code from natural language prompts. Early users (and ex-skeptics) began to report almost uncanny experiences of “AI pair programming.” As one developer put it, *“It’s like you can take inspiration and convert it into something”* tangible in code. By February 2025, the term “vibe coding” had gone viral on Reddit and Slack channels. Y Combinator even released a 30-minute video titled *“Vibe Coding Is the Future”* to explain and promote the concept. While some still see it as a fad or joke, many others – from hobbyists to seasoned engineers – are intrigued by the promise of a more fluid, enjoyable way to build software. To clarify, vibe coding isn’t about writing sloppy code or forgoing structure entirely. It’s about prioritizing the *experience* of coding – maintaining momentum and creative energy – by offloading the drudge work to AI. In traditional coding, you might meticulously research an API, write boilerplate, and debug syntax issues. In vibe coding, you instead describe your intent or sketch a solution in broad strokes, and the AI takes care of the formal implementation, allowing you to iterate rapidly on ideas. One practitioner described it as entering a state where *“the main limitation is not my technical knowledge, but my imagination”*. They note ([What’s the vibe around vibe coding? | IBM](https://www.ibm.com/think/news/vibe-coding-devs#:~:text=Director%20Andrej%20Karpathy%20recently%20used,%E2%80%9D))est for small-scale projects or prototypes without strict business-critical requirements – in those scenarios, it’s a great way to *experiment, learn, and create* with minimal friction. It’s important to acknowledge that vibe coding is a new paradigm and still imperfect. Early adopters have shared both excitement and caution. For example, IBM’s developers observed that current AI tools sometimes produce errors or require the user to step in and guide them; vibe coding *“might be the future, but it’s still imperfect today”*. On developer forums, some warn that leaning too much on AI can become *“gnarly or reckless”* if you merge code you don’t understand. The healthy view is to treat vibe coding as **collaboration**: you bring the creative vision and critical thinking, the AI brings speed and knowledge, and together you build something neither could alone. With that perspective, even skeptics agree it *“is a thing”* – a real emerging practice that can accelerate development and even make coding more accessible to those who aren’t experts. In the next section, we’ll introduce Cursor IDE, one of the tools at the center of this movement, and see how it evolved to enable vibe coding. ## 3. History & Evolution of Cursor IDE Cursor IDE is a young but rapidly evolving player in the IDE landscape, born from the idea that an “AI-native” coding environment could supercharge developer productivity. The company behind Cursor is **Anysphere**, a research-focused startup founded in 2022 by a group of MIT graduates. Their vision was bold: build a brand-new code editor from the ground up with AI at its core, effectively challenging the incumbent coding toolchains. Cursor was launched to the public in 2023 as Anysphere’s first product. Early versions were admittedly rough – one early user recalled that *“when Cursor first came out, it was quite crappy”*, but the team iterated quickly and the product improved dramatically over the next year. By late 2023 and into 2024, Cursor started *“making waves on social media because of its killer product experience”* and steadily gained converts from traditional editors like VS Code. From the beginning, Cursor set itself apart by deeply integrating large language models into the IDE. In fact, Cursor is built as a fork of Visual Studio Code, meaning it inherited the familiar UI and extensibility of VS Code, but with AI capabilities woven into its fabric. This design choice was deliberate – developers could switch to Cursor and *“barely tell the difference”* in the interface, lowering the adoption barrier. Under the hood, however, Cursor was doing something novel: running an AI assistant that knows your entire codebase and can write or edit code via natural language commands. The Cursor team initially integrated OpenAI’s GPT models (like GPT-4) and later also Anthropic’s Claude for AI completions. By 2024, they were also experimenting with their own or fine-tuned models (codenames like “Claude’s Sonnet” or others) to improve speed. This focus on model optimization paid off – features that would have been too slow with earlier AI models became feasible as newer models (e.g. a hypothetical *“GPT-4 Turbo”* or *“Llama 3”*) offered faster responses. **Timelin ([Hot take: Vibe Coding is NOT the future : r/ChatGPTCoding - Reddit](https://www.reddit.com/r/ChatGPTCoding/comments/1iueymf/hot_take_vibe_coding_is_not_the_future/#:~:text=Hot%20take%3A%20Vibe%20Coding%20is,it%20the%20other%20way%20around))milestones:** In the first half of 2023, Cursor’s primary feature was an AI chat sidebar within the editor – essentially ChatGPT inside VS Code – allowing users to ask questions about code or generate snippets. Over 2023, the team added more capabilities like inline code editing and whole-file refactoring by AI. By mid-2024, they introduced a feature called the *Composer* (a panel for direct AI-guided code edits). A pivotal update came in late 2024 with the release of **Agent Mode** (December 2024), which enabled the AI to autonomously perform multi-step coding tasks and tool usage without constant prompts. Agent Mode, as we’ll see, is like having an auto-pilot for coding tasks. Each of these iterations nudged Cursor closer to the ideal of an “AI pair programmer” fully integrated into the development workflow. The rapid evolution of Cursor was fueled not only by technology but also by significant community and financial support. Anysphere received early backing from the OpenAI Startup Fund – an $8M seed investment in October 2023 – reflecting confidence in the idea of an AI-driven IDE. In August 2024, they raised a \$60M Series A led by top venture firms (a16z and Thrive), and by December 2024 a \$105M Series B. These investments coincided with explosive user growth. Cursor adopted a *freemium* model where anyone could try a generous free tier (e.g. 2,000 AI code completions) and then upgrade to a Pro plan for unlimited use. This strategy worked phenomenally: by late 2024, Cursor had over **360,000 paying users** (individual developers paying \$20–40/month) and had astonishingly grown from \$1M to \$100M in annual recurring revenue within 12 months. This made Cursor *“one of the fastest-growing SaaS startups in history”*, outpacing even enterprise SaaS legends in reaching that revenue milestone. Such growth was driven by word-of-mouth among developers. Engineers at companies like OpenAI, Midjourney, Shopify, and others started using Cursor and enthusiastically recommending it. The VS Code compatibility helped Cursor spread virally (“import your extensions and settings in one click” meant no lost productivity switching over). Users were impressed that a tiny startup could compete with giants like Microsoft’s GitHub Copilot – but Cursor’s approach of an *AI-native IDE* rather than just a plugin gave it an edge in user experience. For example, one user from Google exclaimed after a day of use, *“I’m blown away… it’s how Copilot should feel. I’m completely off VSCode now.”*. Many cited tangible productivity boosts: *“Cursor is at least a 2x improvement over Copilot… an incredible accelerator for me and my team,”* said an Instacart engineer. Another early adopter described Cursor’s AI suggestions as *“occasionally so magic it defies reality – ~25% of the time it’s anticipating exactly what I want… enough to make you believe eventually you’ll code at the speed of thought.”* These glowing testimonials hint at why Cursor gained a passionate following among developers seeking that *vibe coding* flow. From a community perspective, Cursor also fostered engagement through forums and by open-sourcing parts of its platform. Notably, Cursor championed an open protocol called MCP (Model Context Protocol) to let developers extend the IDE with custom AI tools – we’ll cover this in depth in Section 11. By enabling community-built extensions and being transparent about how the AI integration works, Cursor created a growing ecosystem around its product. In short, the evolution of Cursor IDE is a story of **fast-paced innovation**: it went from a rough AI experiment to a polished, widely used development environmen ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=,Cursor%20is%20SOC%202%20certified)) a year and a half. The upcoming sections will break down Cursor’s feature set and show how it realized the dream of vibe coding through concrete tools and design decisions. ## 4. Deep Dive into Cursor IDE Features Cursor packs in a rich set of features that blend the familiarity of a modern code editor with the intelligence of AI. If you’ve used VS Code or similar IDEs, the interface layout will feel comfortable: file explorer on the left, code editor tabs, a terminal, etc. But you’ll quickly notice additions – panels and prompts – that reveal Cursor’s AI superpowers. Let’s explore Cursor’s key features and what makes them special, fro ([Cursor – Installation](https://docs.cursor.com/get-started/installation#:~:text=Keyboard%20shortcuts))ve coding to the AI chat that knows your whole project. ### **AI-Powered Code Autocomplete (“Tab”)** One of Cursor’s headline capabilities is its context-aware code autocomplete, triggered simply by pressing the **Tab** key. While basic tab-completion is common, Cursor’s version is far more advanced. It doesn’t just complete the current line – it can predict multiple lines or even an entire code block ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=,Cursor%20is%20SOC%202%20certified)) likely to write next. As you type, Cursor’s AI considers your project context and recent edits to suggest the next logical snippet of code. Users often describe it as coding with an expert looking over your shoulder, guessing what you need before you finish typing. For example, if you start writing a function signature `def maximum(a, b):` in Python, Cursor might ([Cursor – Overview](https://docs.cursor.com/chat/overview#:~:text=Default%20mode))gest the implementation of returning `a if a > b else b` – you can accept it with Tab in one keystroke. It even works from natural language comments; if you write a comment “// loop through all pairs in list,” Cursor might generate the nested loop code for you. This goes beyond syntax – it’s using AI understanding to infer inten ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=,Cursor%20is%20SOC%202%20certified))ers to Cursor’s Pro plan get an even more powerful version of this feature, where the AI’s “Tab” suggestions draw on larger context windows (allowing it to coordinate edits across a file). In practice, developers find that *multi-line Tab completions* accelerate boilerplate coding significantly and help maintain flow. The editor also does **smart rewrites** on the fly – if you write something that’s slightly off, the AI might automatically adjust or correct it (for instance, fixing a common mistake or adding a missing parameter). This reduces trivial errors and keeps your code clean. ### **Inline Editing & Composer** Cursor enables a novel way of editing code: by instructing the AI in plain English **within the editor**. Instead of manually cutting, pasting, and rewriting sections, you can highlight a block of code and tell Cursor what you want changed. This happens in the **Composer** (formerly called Edit mode in some docs). For instance, you might select a function and type “Optimize this function’s performance” – Cursor will analyze it and suggest an improved version, showing the diff (red deletions and green insertions) right inline. You can then accept or reject those changes. Unlike a separate chat, the Composer applies changes immediately to the file, which makes it a very direct way to refactor code. One key difference from ChatGPT plugins: *the edits are applied with your approval but without needing to copy-paste*, and you see exactly what changed with diff highlighting. Cursor’s inline edit interface even allows you to pick which AI model to use from a dropdown (e.g. GPT-4 vs a faster 3.5 model) before generating the edit. Typical uses of inline editing include: translating code from one language to another, improving variable naming, inserting error handling, or updating a deprecated API usage – all by simply asking Cursor to do it. This feature is available to all users (not just pro), making AI-assisted refactoring accessible out-of-the-box. It feels like a powerful find-and-replace on steroids, guided by understanding of code logic. ### **AI Chat (Ask Mode)** On the right side of Cursor’s UI lies the **Chat sidebar**, which is essentially your conversational AI assistant. This is where vibe coding truly comes alive, as you can have a back-and-forth dialog with an AI that *“understands your entire codebase”* context. By default, Cursor’s chat operates in **Ask mode**, meaning it will answer questions and make suggestions *without automatically modifying your code*. You might ask things like “What does this function do?”, “Why am I getting this error?”, or “How can I implement feature X?” The AI has access to your open files and a local index of your repository, so it can refer directly to your code in its answers. For example, you can highlight a piece of code and ask, “Is there a bug here?” – the AI will analyze it in context and respond with an explanation or potential issue if it finds one. This is immensely helpful for understanding unfamiliar code or debugging. It’s like having the combined knowledge of Stack Overflow and your project documentation at your fingertips, conversationally. Some special powers of Cursor’s chat include **cod ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=,Early%20Access%20Program)) ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=The%20Model%20Context%20Protocol%20,and%20tools%20through%20standardized%20interfaces))t inclusion**. Using *@-mentions*, you can inject specific files or symbols into the question. Typing `@` in the chat brings up an autocomplete of file names or functions in your project; select one, and it’s attached (the interface shows these as chips or references). The AI then knows to focus on those. You can even ask global questions by hitting Ctrl+Enter (Cmd+Enter) to search the entire codebase for relevant snippets to include. This is what Cursor calls **Codebase answers** – the assistant will cite where in your files an answer is found. For instance, “What is the latest version of library X used in this project?” could trigger it to scan your requirements.txt or package.json and answer accordingly. Another neat feature: **documentation integration**. Cursor allows referencing popular libraries by name (e.g. `@Pandas` or `@React`) which pulls in their official docs for context. You can also add your own markdown docs via `@Docs`. This means the AI can answer questions like “How do I use function Y from library Z?” with actual documentation context, and even your project’s README if you’ve added it. Finally, the chat supports **multimodal input**. You can drag and drop an image into the chat – say a screenshot of an error or a UI mockup – and the assistant will consider it. For example, developers have pasted UI design screenshots and asked Cursor to generate the corresponding HTML/CSS, which it can do by “seeing” the image layout. This is cutting-edge and feels like sci-fi: describing a GUI in natural language or via an image and getting runnable code. (As of late 2024, image input worked in normal chat mode; a known bug temporarily broke it in composer mode but was being fixed.) There’s also a **Web Search** tool accessible via `@Web`, allowing the AI to fetch up-to-date information from the internet within the chat. This is useful for checking latest library versions or grabbing an example from the web. In short, Cursor’s Ask-mode chat is an AI assistant that you can converse with, *with full awareness of your coding context and even external info*. It’s great for Q&A, brainstorming solutions, or getting explanatory help without the AI changing your code unless you explicitly apply a suggestion. ### **Agent Mode (Autonomous AI Coding)** For mor ([Cursor IDE: Adding Memory With Graphiti MCP ⚡️ - Zep](https://www.getzep.com/blog/cursor-adding-memory-with-graphiti-mcp/#:~:text=Cursor%20IDE%3A%20Adding%20Memory%20With,preferences%2C%20standards%2C%20and%20specs)) ([Show HN: Cursor IDE now remembers your coding prefs using MCP](https://news.ycombinator.com/item?id=43506068#:~:text=MCP%20news,significant%20value%20for%20your))offers **Agent mode**, effectively an “auto-pilot” for coding. When you switch the chat into Agent mode, you empower the AI to *take actions* on its own to complete a goal. In Agent mode, the AI has all the tools enabled – it can edit files, create new files, run terminal commands, and use any integrated tools (more on tools soon) in order to accomplish what you asked. This mode was introduced in late 2024 and became the default mode by v0.46 in early 2025. Think of it as telling a junior developer, “Go ahead and implement this feature; you have the run of the codeba ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=Blog%20Blog%20Iterating%20with%20Shadow,by%20Arvid%2019%20minutes%20read))sk me if you’re unsure.” For example, you can prompt Agent mode: “Add a new API endpoint `/users` that returns a list of users from the database.” The Cursor agent will then create the corresponding file or function, write the code, perhaps even add a database query, and modify configuration files if needed. It plans a multi-step solution and executes it, occasionally pau ([Cursor AI: A Guide With 10 Practical Examples | DataCamp](https://www.datacamp.com/tutorial/cursor-ai-code-editor#:~:text=Cursor%20also%20integrates%20advanced%20chat,features%20to%20facilitate%20better%20interaction))r confirmation if it’s about to do something major like run a command or if it needs clarification. Under the hood, what Agent mode does is perform a chain of reasoning with the AI. It can use *tools* such as searching the codebase, reading documentation, or running the project’s tests to verify its changes. One of Cursor’s unique capabilities here is editing multiple files in one go. The agent might decide, for instance, “I need to modify both `routes.py` and `models.py` to add this feature,” and it will do so in one session – something traditional AI coding assistants couldn’t reliably do before. Cursor’s creators identified this as a key differentiator: *“AI’s ability to modify multiple parts of a single file or multiple files at the same time”* is what sets Cursor’s product experience apart. Agent mode is exactly that brought to life. It automatically uses retrieval to *“learn your codebase”* so you don’t have to feed it context manually, and it will even execute shell commands to test or lint the code as it works (with your permission). By default, if the agent wants to run code (say run `npm test` or a Python script), it will prompt you to approve (to avoid any destructive actions). You can enable a **“Yolo mode”** to let it run tools without asking every time – essentially trusting the agent fully, which some power users do when they’re iterating quickly. It’s worth noting that Agent mode is powerful but also where the *vibe* needs some oversight. Many users find it brilliant for boilerplate and tedious tasks, but they still guide it with careful prompts. One early adopter on Reddit mentioned using Agent mode for about 6 weeks before switching back to a more manual approach for large codebases, because the agent could make sweeping changes that weren’t always desired. The Cursor team is continuously improving the agent’s reliability – for example, they added an **“auto-fix error loop”** where if the code the agent writes throws an error, the agent can catch that and attempt a fix in the next iteration automatically. This gives a glimpse of the future: an AI that can debug itself. In practice today, Agent mode is excellent for tasks like: *scaffolding* a new project module (it’ll create multiple files), performing a large refactor (rename a function across dozens of files), or tackling a well-specified feature end-to-end. Always review its changes – Cursor makes that easier by presenting diffs and letting you undo – but when used wisely, Agent mode can feel like having a diligent coding assistant working asynchronously on your project. ### **Integrated Developer Tools and UI** Aside from the AI coding features, Cursor IDE offers all the conveniences you expect from a modern environment, often enhanced by AI. It has built-in **Git integration** and can even draft commit messages for you using AI (Cursor’s “AI Commit Message” feat ([Behind Cursor’s Success: Two PMFs | by Gru.ai | Medium](https://medium.com/@connect_33559/behind-cursors-success-two-pmfs-524051f2aa35#:~:text=Cursor%20received%20angel%20investment%20from,60%20million%20in%20August%202024)) ([How did cursor grow so fast -$1M to $100M ARR in 24 Months](https://www.productmarketfit.tech/p/how-did-cursor-grow-so-fast-1m-to#:~:text=Series%20B%20,led%20by%20Thrive%20Capital))hanges into a commit message on demand). It supports **debugging, terminals, and all languages** that VS Code does, since under the hood it’s compatible with VS Code extensions for language support. You can install any VS Code extension in Cursor – the website touts that you can *“import all your extensions, themes, and keybindings in one click”* so it *“feels familiar”*. This means if you need a Docker extension or Python linting, you can add those just as you would in VS Code. A standout innovation by Cursor is the concept of **Shadow Workspaces**. This is a safety net for vibe coding: when the AI (especially in Agent mode) makes changes, it can do so in a “shadow” copy of your workspace that doesn’t immediately alter your actual files. Only when you click apply/accept do the changes merge into your real code. This feature lets you use the AI boldly without fear of it wrecking your project – you always have final control. As one LinkedIn reviewer noted, *“When the AI generates code, there’s an ‘Apply’ button that pulls in the code, complete with diff highlighting and an Accept button. The flow is so much easier and safer… there’s much more clarity as to what exactly has been changed.”*. Shadow workspaces essentially implement a staging area for AI changes, which is great for trust and verification. Cursor also provides **resource management tools** in the UI, like a **Process Explorer** to see if any background processes or extensions are consuming too much CPU/RAM. This is handy since running AI can be heavy; you can diagnos ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=What%20is%20MCP%3F))e issues (for example, it might reveal an extension that’s misbehaving and you can disable it). The **Settings** in Cursor’s GUI allow you to configure AI-related options – which model to use by default, whether to use the cloud or local model (if available), enabling “Privacy Mode” (which ensures your code is not sent to remote servers for AI – important for sensitive projects), and toggling exp ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=What%20is%20MCP%3F))atures. In summary, Cursor IDE’s feature set includes everything a programmer needs *plus* an AI copilot that can predict code, explain and edit code on command, and even take the wheel to implement features. It merges the worlds of traditional IDE tooling (debuggers, linters, version control) with cutting-edge AI assistance (chat, autocompletion, autonomous agent). This tight integration is what allows **vibe coding** to happen – you can stay in flow, converse with your code via the AI, and trust a lot of the grunt work to Cursor. Next, we’ll discuss how to embrace this new philosophy practically: how to set up your workspace and mental approach for vibe coding success. ## 5. Embracing the Vibe Coding Philosophy Adopting *vibe coding* is as much about mindset and workflow as it is about tools. In this se ([Cursor AI: A Guide With 10 Practical Examples | DataCamp](https://www.datacamp.com/tutorial/cursor-ai-code-editor#:~:text=Cursor%20also%20integrates%20advanced%20chat,features%20to%20facilitate%20better%20interaction))xplore how you can cultivate the right practices and environment to make the most of Cursor IDE as a vibe c ([Cursor MCP — A 5-Minute Quick Start Guide | by Yehuda Levi | Feb, 2025 | Medium](https://medium.com/@levi_yehuda/cursor-mcp-a-5-minute-quick-start-guide-3c6f214557d5#:~:text=,time%20without%20leaving%20Cursor%20IDE)) blend of setting up your workspace for flow, learning how to communicate effectively with the AI, and adjusting your development habits to balance creativity with oversight. **5.1 Mindset and Practices:** At ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Databases)) ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Let%20Cursor%20create%20PRs%2C%20create,branches%2C%20find%20code%2C%20etc))a shift in mindset from strict control to creative collaboration. You’ll want to approach coding sessions more flexibly. Instead of writing a detailed design document or outline for every function upfront, give yourself permission to *improvise with the AI*. For example, you might start a project by describing what you want in natural language inside Cursor, and let the AI generate an initial structure. This doesn’t mean abandoning all planning – it means iterating in smaller loops guided by what *feels* right or interesting to ([How I Made Cursor AI 10x More Powerful using MCP - YouTube](https://www.youtube.com/watch?v=EPDCuJIrMyI&vl=en#:~:text=How%20I%20Made%20Cursor%20AI,with%20the%20Zapier%20MCP%20server))du Ferro Aldama, a developer who practices vibe coding, noted that he *“didn’t start with a strict plan”* when building two small apps; he had a general idea and *“let curiosity drive the process”*, step by step. That captures vibe coding well: you know roughly where ([Cursor IDE: Adding Memory With Graphiti MCP ⚡️ - Zep](https://www.getzep.com/blog/cursor-adding-memory-with-graphiti-mcp/#:~:text=Cursor%20IDE%3A%20Adding%20Memory%20With,preferences%2C%20standards%2C%20and%20specs)) ([Show HN: Cursor IDE now remembers your coding prefs using MCP](https://news.ycombinator.com/item?id=43506068#:~:text=MCP%20news,significant%20value%20for%20your))’re willing to follow the AI’s lead at times or explore tangents that emerge during the flow. It can be a very **iterative and experimental process**, often with surprising results (sometimes great, sometimes not – but even the failures teach you and the AI something). To embrace vibe coding, treat the AI as a partner. This means asking for its input frequently and conversing with it as you would with a human pair-programmer. For instance, try “thinking out loud” in the chat: *“I’m considering using a binary search here, what do you think?”* or *“This function feels slow, any ideas to optimize?”* – you may be delighted at the useful suggestions you get. It also means being open to the AI’s output even if it’s not exactly what you envisioned – perhaps it suggests a different algorithm or a creative UI design. Vibe ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Uses)) ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Let%20Cursor%20create%20PRs%2C%20create,branches%2C%20find%20code%2C%20etc))tion**. One IBM developer mused that vibe coding is like taking inspiration and immediately ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Cursor%20supports%20two%20transport%20types,for%20MCP%20servers)) ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Input%3A%20Valid%20shell%20command%20that,is%20ran%20by%20Cursor%20automatically))oftware. To do that, you should let the AI try things. If it makes a mistake or you don’t like the direction, you can always undo or steer it differently (like telling it “No, let’s approach it this other way” in chat). Maintain a dialogue; you are effectively pair programming with an AI that has endless patience and significant knowledge. **5.2 Workspace Setup:** While vibe coding is more about how you work than where, a comfortable and focused environment can amplify your flow. Cursor IDE itself provides some features to aid concentrat ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=MCP%20servers%20can%20be%20written,and%20technology%20stack%20very%20quickly))ple, you might enable a **distraction-free mode** or use a clean dark theme if that helps you “get in the zone.” Since vibe coding often involves rapidly switching between writing, prompting, and reading AI outputs, arrange your editor layout to make that easy. Many vibe coders keep the AI chat panel open at all times on a side screen or split, so it feels like part of the coding canvas rather th ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=SSE%20Transport))ught. You could also configure Cursor’s *“Default chat mode”* to Agent or Ask depending on your preference (Cursor allows setting which AI mode is the default when you open the chat). If you foresee doing a lot of autonomous agent work, set it to Agent by default; if you mostly ask questions, keep it in Ask mode – this just saves a bit of friction. Personalization can help set a creative mood. Cursor supports importing VS Code themes, so choose one that you find inspiring or calming. Some developers even coordinate background music with their coding sessions to maintain a rhythm – that’s ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Tool%20Approval))ope of Cursor itself, but as a vibe coder, don’t be afraid to cater to your senses (after all, *“vibe”* is literally in the name). The key is to minimize anything ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Tool%20Approval))our flow. Configure autosave or live share of code if needed so you’re not manually managing files. Use the built-in **Notepads** feature (still in beta) as a quick scratchpad for ideas or prompts you want to refine. For example, you can jot down a list of features or steps in a Notepad, then sequentially feed them to the AI. Another aspect of ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=You%20can%20place%20this%20configuration,depending%20on%20your%20use%20case)) ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Global%20Configuration))ect context**. Since the AI works best with knowledge of your codebase, it’s smart to ensure your project i ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=%2F%2F%20Cursor%20automatically%20runs%20this,)) ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Input%3A%20URL%20to%20the%20,MCP%20server%20external%20to%20Cursor))tant guidelines are given to Cursor. Cursor automatically indexes your codebase locally for search, but you can en ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=%22server,%7D%20%7D%20%7D))by creating a `.cursorrules` or similar file with high-level notes. Some users write a brief project summary or coding style notes and feed it to the AI at the start (Cursor’s “Rules for AI” feature allows specifying such guidelines). For instance, you might inform it “We use functional components over classes in this React project” or “Follow PEP8 style in all suggestions.” By aligning the AI with your project’s vibe, you get more coherent assistance. In one example, a developer dumped an entire README and some design notes into the AI’s context at project start, finding that *“it works pretty well — especially when bootstrapping the site from scratch”*. This kind of upfront context-setting can pay off throughout your vibe coding session. **5.3 Balancing Creativity with Control:** A common question is how to stay *in control* of your code quality and architecture when vibe coding encourages a free-flowing approach. The answer is balance – enjoy the cr ([Cursor – Model Context Protocol](https://docs.cursor.com/context/model-context-protocol#:~:text=Using%20MCP%20Tools%20in%20Agent)) but periodically review and tidy up. It’s similar to how one might do quick, rough drafts in writing and then edit for clarity. In vibe coding, you might rapidly generate five new functions with the AI in an hour (things that might have taken you a day manually), but then spend some time going through them, adding comments, writing tests, and refactoring any bits that seem off. It’s a good practice to alternate between **“creative mode”** and **“critical mode.”** When you’re in creative mode (perhaps using Agent mode or just brainstorming in chat), let the ideas flow without overanalyzing. When you switch to critical mode, slow down and scrutinize the output: run your test suite, step through a debugger if needed, or ask the AI to explain the code back to you to ensure you understand it. Remember that vibe coding doesn’t eliminate fundamental software engineering—requirements analysis, algorithmic thinking, and design still matter. Use the AI to amplify your abilities, not replace them. For example, if you’re building a feature, you might first outline the user story or acceptance criteria (possibly having the AI help format or flesh those out), then vibe code the implementation, and finally double-check that the result meets the criteria. The AI can help at each stage, but you steer the high-level direction. It’s similar to how a senior developer might guide a junior: you let the junior write the first pass (here the AI is the junior), but you then perform a code review. Many Cursor users indeed use the AI for first drafts and then do a manual pass. You can even ask Cursor’s chat to *review its own code* by prompting, “Are there any potential issues with the code you just wrote?” – it will often catch things like missing null checks or performance concerns and suggest improvements, which you or it can then implement. **5.4 Cultural Reflections:** Embracing vibe coding also means being part of a shift in developer culture. It encourages collaboration and knowledge sharing in new ways. Don’t hesitate to share interesting prompts or techniques on the Cursor forum or Discord; the community is collectively learning how to best work with AI. It’s common to see developers exchanging “prompt patterns” or small script-like prompts that yield great results. For example, a prompt like *“Refactor this code for clarity and add comments explaining each step”* could be a reusable pattern you share. This communal aspect – almost like sharing recipes – is a fun part of vibe coding culture. Finally, keep an open yet critical mind. **Vibe coding philosophy celebrates creativity and flow**, but it doesn’t idolize AI as infallible. The best vibe coders develop a keen intuition for when to trust the AI’s suggestions and when to question them. Over time, as you work with Cursor, you’ll get a sense of its strengths (e.g. cranking out standard code quickly) and its weaknesses (e.g. it might not understand nuanced business logic without guidance). Use that knowledge to your advantage. A useful habit is to regularly ask *“Why?”* – if Cursor proposes an unusual approach, ask it to explain its reasoning. A true flow state in vibe coding isn’t one where you blindly accept everything, but one where you and the AI are in sync and bouncing ideas off each other. When that happens, coding truly starts to feel like a creative art form. You’ll find yourself in a rhythm where the *“vibe”* is right – you’re solving problems fluidly, with Cursor handling the boilerplate and you focusing on the vision. That’s the ideal we’re aiming for as we now turn to practical tutorials and exercises. ## 6. Tutorials & Hands-On Guides Let’s get practical! In this section, we’ll walk through getting started with Cursor IDE and dive into some hands-on exercises to build your vibe coding skills. By following these guides, you’ll go from installation to writing your first AI-assisted code, and even create a small project with Curs ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Check%20Your%20Extensions)) ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Use%20the%20Process%20Explorer))g step by step on your own machine. ### **6.1 Insta ([Cursor MCP — A 5-Minute Quick Start Guide | by Yehuda Levi | Feb, 2025 | Medium](https://medium.com/@levi_yehuda/cursor-mcp-a-5-minute-quick-start-guide-3c6f214557d5#:~:text=What%20is%20MCP%3F)) ([Cursor MCP — A 5-Minute Quick Start Guide | by Yehuda Levi | Feb, 2025 | Medium](https://medium.com/@levi_yehuda/cursor-mcp-a-5-minute-quick-start-guide-3c6f214557d5#:~:text=In%20this%20guide%2C%20we%E2%80%99ll%20create,tool%20will%20allow%20us%20to))tting Cursor up and running is straightforward. The IDE is available for Windows, macOS, and Linux, and the installation process will feel familiar. 1. **Download Cursor:** Head to the [official Cursor website](https://cursor.com) and click the “Download” button. Cursor provides n ([Cursor MCP — A 5-Minute Quick Start Guide | by Yehuda Levi | Feb, 2025 | Medium](https://medium.com/@levi_yehuda/cursor-mcp-a-5-minute-quick-start-guide-3c6f214557d5#:~:text=,time%20without%20leaving%20Cursor%20IDE)) ([Cursor MCP — A 5-Minute Quick Start Guide | by Yehuda Levi | Feb, 2025 | Medium](https://medium.com/@levi_yehuda/cursor-mcp-a-5-minute-quick-start-guide-3c6f214557d5#:~:text=In%20this%20guide%2C%20we%E2%80%99ll%20create,tool%20will%20allow%20us%20to))h OS (EXE for Windows, DMG for Mac, DEB/RPM for Linux, etc.). Download the one appropriate for you. 2. **Install:** Run the installer and follow the prompts. - On Windows, this means running the `.exe` and accepting any security prompts. - On macOS, open the downloaded `.dmg` and drag **Cursor.app** into your Applications folder. - On Linux, install the package via your package manager (for example, on Ubuntu: `sudo dpkg -i cursor.deb`). Installation is usually quick. Once done, launch Cursor from the Start menu (Windows), Applications (Mac), or via the `cursor` command if you added it to your PATH. 3. **First Launch Configuration:** When Cursor first opens, it may prompt you with a quick setup wizard. You can choose your preferred **keyboard shortcuts** scheme – e.g. if you are coming from VS Code, select the VS Code shortcuts (that’s the default). If you’re used to another editor like Sublime or Vim, you might import those keybindings. Next, you’ll be asked about AI language preferences; by default, Cursor communicates in English, but you can set another la ([Cursor MCP — A 5-Minute Quick Start Guide | by Yehuda Levi | Feb, 2025 | Medium](https://medium.com/@levi_yehuda/cursor-mcp-a-5-minute-quick-start-guide-3c6f214557d5#:~:text=,time%20without%20leaving%20Cursor%20IDE))he AI’s responses if you prefer. You’ll also be prompted to log in or create a Cursor account – go ahead and do so (you can use an email or possibly GitHub login). Having an account is required since Cursor’s AI features use cloud services that track usage. The free tier will be active by default, which is fine for now. 4. **Take the Tour:** Cursor often presents an **interactive tour** for new users. It’s a great idea to go through it. The tour will highlight where the Chat panel is, how to open the Composer, and show a sample AI query. In a few minutes, you’ll see, for example, how to ask the AI a question about a piece of code and how to apply a suggestion. After this, you should see the main editor interface ready for you to open a project. **Opening a Project:** You can open an existing folder of code (File -> Open) or start a new project. If you start fresh, create a new folder for your project and open it in Cursor. Let’s do that for our tutorial: create a folder called `vibe-todo-app` (we’ll make a simple to-do list CLI in Python for practice). Once open, you’ll see an empty workspace. Before coding, notice the right-side panel with two tabs: **Chat** and **Composer**. Click on Chat – you’ll be greeted by the AI assistant interface. Also notice along the bottom or top a toolbar with an icon for Agent mode vs Ask mode (depending on Cursor’s version, you might see a toggle or a dropdown to switch AI modes). ### **6.2 Your First AI Query** Let’s dip our toes in by using Cursor as an interactive assistant. In the Chat panel, you should see a prompt area where you can type. Try something simple to get comfortable. For example, type: ``` What is vibe coding? ``` And hit enter. This is a plain question to the AI (in Ask mode). Cursor will think for a moment and then provide an answer in the chat, likely explaining the co ([MCP Servers for Cursor - Cursor Directory](https://cursor.directory/mcp#:~:text=MCP%20Servers%20for%20Cursor%20,MCP%20servers%20within%20Cursor%20IDE))be coding. You should see that it formats the answer in markdown if needed, and you can copy text out if you want. This confirms your AI is working. Yo ([21st-dev/magic-mcp: It's like v0 but in your Cursor/WindSurf ... - GitHub](https://github.com/21st-dev/magic-mcp#:~:text=Magic%20Component%20Platform%20,instantly%20through%20natural%20language))y question here – it’s like having Stack Overflow and a programming guide built-in. For instance, try asking about Python: ``` How do I sort a list of dictionaries by a key in Python? ``` The AI will respond, possibly with a brief explanation and a code snippet using Python’s `sorted` function and a lambda or `itemgetter`. You didn’t have to leave your editor or search the web – this is already a productivity win. ### **6.3 Writing Code with Autocomplete** Next, let’s see Cursor’s autocomplete in action. In your empty project, create a new file `todo.py`. We’ll start writing a simple to-do list program, but using vibe coding principles, we won’t write everything ourselves – we’ll prompt the AI to help. Begin by defining a function: ```python def add_task(tasks, task): ``` After typing this line and hitting Enter, pause for a second. Cursor’s Tab feature might kick in with a suggestion. It knows you started a function to add a task, so it might propose something like: ```python tasks.append(task) return tasks ``` (as an example). If it does, you can press **Tab** to accept the suggestion. Hooray – you just wrote a function with minimal keystrokes. If no suggestion appears, you can invoke it by pressing Ctrl+Space to see suggestions or simply continue writing and it may complete the line. Now type a comment or docstring to describe what the function should do, for instance: ```python """Add a task to the list of tasks.""" ``` Even this might trigger the AI to infer the implementation. The more context you give (like variable names and comments), the better predictions it makes. Let’s purposely write a slightly high-level instruction and let the AI handle implementation details. Under the function, type: ```python # Ensure no duplicate tasks ``` and press Enter. Cursor may complete this thought by generating code to check for duplicates. It might suggest: ```python if task in tasks: return tasks ``` Press Tab to accept. Then perhaps it continues to the append line. Essentially, you can guide the AI with comments. This is a great way to *describe what you want* (the vibe) and have Cursor fill it in. It’s okay if you don’t get a suggestion every time; you can always write manually and the AI will catch up in subsequent lines. Continue writing a couple more simple functions in `todo.py` to practice: ```python def list_tasks(tasks): # Print all tasks with indices ``` After typing the comment, expect a suggestion to loop through tasks and print them (Cursor might generate a `for i, t in enumerate(tasks): print(i, t)` kind of construct). Accept it. Then: ```python def remove_task(tasks, index): # Remove task at the given index safely ``` Likewise, Cursor should suggest using `pop` or something with a bounds check. Through these small exercises, you’re seeing how *natural language and minimal code input can yield full implementations*. This is vibe coding in microcosm – the boundary between writing code and writing intent blurs. ### **6.4 Using the AI Composer for Refactoring** Now let’s try the **Composer** for a refactoring task. Suppose we realize that our `list_tasks` function prints tasks, but we want it to return a string instead (maybe for logging). Instead of manually rewriting it, we’ll ask Cursor. Highlight the body of `list_tasks` (the for-loop it created). Then either right-click and choose an AI action or use a keyboard shortcut to open the Composer with that selection (often **Ctrl+K** in Cursor focuses the AI palette). In the Composer prompt that appears, type something like: *“Modify this function to return a formatted string of all tasks instead of printing them.”* Then hit the generate/apply button. Cursor’s AI will think and then ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Some%20users%20see%20high%20CPU,warnings%20about%20high%20RAM%20usage)) ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Check%20Your%20Extensions))place the print loop with building a string (`"\n".join([...])`) and return it. Review the diff – it should align with what we asked. If it looks good, click **Apply**. The code in `todo.py` will be updated. Congratulations, you just refactored code ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Check%20Your%20Extensions)) instruction! The Composer is extremely useful for tasks like renaming a variable across a function, optimizing a snippet, or inserting error handling. Try another quick one: in `remove_task`, may ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=The%20Process%20Explorer%20is%20a,which%20processes%20are%20consuming%20resources)) ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=If%20the%20process%20is%20listed,and%20disable%20the%20problematic%20extension))pdated list. Highlight that function and prompt, *“Make sure this function returns the updated tasks list after removal.”* The AI will likely add a `return tasks` at the end. Apply that change. ### **6.5 Building a Small Project Example** Now for a slightly larger example, let’s build out a simple to-do list application workflow using vibe coding techniques. We have the basic functions. Perhaps we want a menu interface where users can add, list, and remove tasks via console input. Instead of writing that logic from scratch, we’ll ask the AI to do it in Agent mode. Open the chat sidebar, and switch to **Agen ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Cursor%E2%80%99s%20extensions%20or%20settings)) for a toggle or dropdown that says Agent). Now type a high-level prompt: ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=If%20you%20are%20seeing%20a,see%20the%20correct%20memory%20usage))mple text-based menu in the main section that allows the user to add a task, list tasks, or remove a task by index. Use a loop that continues until the user quits.`” Then press Shift+Enter (or whatever executes the agent). What will happen: The Cursor agent will likely start editing `todo.py` (or creating a new main section if needed). You might see in real-time as it writes a `if __name__ == "__main__":` block with a menu system. It could do something like use `input()` to get the user’s choice, and call `add_task`, `list_tasks`, etc., accordingly. It will probably also create a list `tasks = []` at the top to main ([Cursor AI: A Guide With 10 Practical Examples | DataCamp](https://www.datacamp.com/tutorial/cursor-ai-code-editor#:~:text=match%20at%20L233%20Image%3A%20cursor,ai%20submit%20edit)) Since this is a multi-step goal, the agent might take a few iterations, possibly running the code to test it. Cursor’s agent might ask for your approval to run the script to ensure it works (especially if you have “auto-run” enabled, it might just do it). Approve if prompted. If all goes well, you’ll end up with a working interactive to-do list program. Now try running it yourself: use Cursor’s built-in terminal to run `python todo.py`. Test adding tasks (“Buy milk”, “Do homework”), listing them, removing one, an ([Cursor – Overview](https://docs.cursor.com/chat/overview#:~:text=Context)) should function. You’ve essentially co-written an entire mini application with the AI in minutes. For learning purposes, consider intentionally making the AI encounter a scenario: for example, what if you input an invalid index when removing? Does it crash? If yes, you can then go back to chat and say, *“The remove_task function crashes on bad index. Handle that gracefully.”* In Agent mode, it will update the function to catch IndexError (perhaps wrapping in try/except). This dynamic back-and-forth is how you develop with vibe coding – run, get feedback (from tests or usage), and immediately instruct AI to fix or improve. ### **6.6 Additional Exercises** Here are a few more mini-exercises to practice various features: - **Exercise: Documentation Generation** – Ask Cursor to document your code. For instance, prompt in chat: *“Generate docstrings for all functions in this file.”* In Agent mode, i ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Networking%20Issues%20)) ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Cursor%20relies%20on%20the%20HTTP%2F2,to%20use%20Cursor%E2%80%99s%20AI%20features))uoted docstrings to `add_task`, `list_tasks`, etc., describing their behavior. This shows off Curso ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=This%20can%20be%20the%20case,using%20a%20proxy%20like%20Zscaler)) to bulk-edit multiple parts of a file. - **Exercise: Unit Test Creation** – Prompt: *“Create a new file test_todo.py with unit tests for the functions in todo.py using pytest.”* The agent should generate a `test_todo.py` with a few test cases for add, remove, list. This demonstrates multi-file creation. Run the tests to verify (you may need to install pytest if not already – the agent might even do that if it notices, by running `pip install pytest`) ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=Networking%20Issues%20)) ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=To%20resolve%20this%2C%20Cursor%20now,HTTP%2F2))ill build confidence in the AI’s code. - **Exercise: Using Web Search** – Try an AI query that requires external knowledge. For example: *“What is the latest version of Django and how do I start a new Django project?”* Without `@Web`, the AI might answer with its trained knowledge (possibly outdated). Now ask with `@Web`: *“@Web Latest Django version and command to start project”*. The response should fetch the current info (like “Django 4.x is the latest; use `django-admin startproject mysi ([Cursor – Common Issues](https://docs.cursor.com/troubleshooting/common-issues#:~:text=I%20see%20an%20update%20on,changelog%20but%20Cursor%20won%27t%20update))emonstrates pulling live info. - **Exercise: Image to Code** – If you’re adventurous, sketch a simple webpage layout (even hand-drawn boxes, or using a tool to draw a wireframe) and save it as an image. Drag it into the chat and ask Cursor to generate HTML/CSS for that design. This uses the image support feature. The result might not be pixel-perfect, but it’s often a solid starting point, and it’s a great showcase of multimodal AI assistance. Throughout these exercises, remember to experiment. Try phrasing things differently to see how the AI responds. If the AI ever does something unexpected or incorrect, use it as a learning moment: ask *why*, or simply hit Undo (Cursor, being based on VS Code, has normal undo/redo which will revert AI changes as well). You can always manually fix things too – vibe coding doesn’t mean you *never* write code, it just means you write the interesting parts and let AI handle the repetitive or obscure parts. By now, you should have Cursor installed, configured, and have gone through a full cycle of using it to create and modify a small project. You’ve seen how to use instructions to generate code, how to refine that code via chat or composer, and how to leverage special tools like web search. In the next section, we’ll step back and discuss how to integrate these practices into effective developer workflows, combin ([Cursor AI: A Guide With 10 Practical Examples | DataCamp](https://www.datacamp.com/tutorial/cursor-ai-code-editor#:~:text=Cursor%20also%20integrates%20advanced%20chat,features%20to%20facilitate%20better%20interaction))of AI with the precision of human oversight. ## 7. Developer Workflows & Best Practices Using Cursor IDE and vibe coding techniques can dramatically speed up development, but to harness that speed effectively, you need a solid workflow. In this section, we’ll outline how to structure your work with Cursor day-to-day and share best practices that experienced users have learned. The goal is to achieve a balance where you get maximum productivity and creativity boosts from the AI, *without* sacrificing code quality or going down rabbit holes. ### **7.1 Workflow Structure** A recommended workflow with Cursor often alternates between **planning, coding with AI, and reviewing** in cycles: - **Plan (High-level):** Start by defining what you want to build or change. This could be a user story, a function specification, or a bug to fix. Write down (or even tell the AI) a brief outline of the steps or compon ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=,Cursor%20is%20SOC%202%20certified)). For example, if adding a new feature, list the sub-tasks (create DB model, API endpoint, UI changes). You don’t need a 10-page spec – just enough breakdown so you can tackle pieces one by one. - **Co ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=,Cursor%20is%20SOC%202%20certified))(Implementation):** Pick the first sub-task and implement it using Cursor’s AI. This is where you jump into vibe coding ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=,Cursor%20is%20SOC%202%20certified))ps use Agent mode to scaffold it, or manually write a stub and use Chat/Composer to fill it in. Focus on one thing at a time so the AI doesn’t get confused. For instance, implement the database model and verify it’s right *before* moving to the API endpoint. This keeps the context clear for the AI and helps isolate issues. - **Review & Test:** After each sub-task, pause and review what was generated. Run tests or the application to see that it works as expected. This is crucial – it’s easy to speed ahead with AI and end up with integration issues later. By testing incrementally, you catch errors when they’re easier to fix. Cursor can help here too: use the AI to write tests (as we did in the tutorial) or to run static analysis. If something fails, you can immediately ask Cursor *why* or *to fix it*. For example, if a test fails, paste the error into chat and the AI can often identify the bug and suggest a patch. Approve that via the Composer or Agent, and continue. - **Iterate:** Move to the next sub-task or improvement and repeat the code+review cycle. Because Cursor makes each cycle so fast, you might do dozens in a day. That’s fine – lean into the rapid iterations. One Cursor user described that with the agent’s help they could iterate on their project at a much faster cadence than before. This iterative flow is where vibe coding shines: you try an approach, see results, adjust, and so on, maintaining momentum. - **Polish:** After the feature is done and tests pass, do a final pass to polish the code. Ensure naming is consistent (you can ask the AI to rename variables for clarity if needed), remove any commented-out junk or debug prints the AI might have left. Possibly format the code (Cursor can use formatters or you can instruct the AI “Clean up the formatting”). Also, consider generating documentation or updating docs at this stage – again, AI can help draft docstrings or even update your README if you prompt it. Throughout this workflow, **version control** ([Cursor – Overview](https://docs.cursor.com/chat/overview#:~:text=,Keyboard%20Shortcuts))ty net. Treat the AI’s major changes like contributions from a teammate: commit often and use branches. It’s good practice to commit after each significant sub-task is completed and tested. If the AI goes off track, you can always revert to last commit. Cursor’s integration with Git (and the AI commit message generator) makes this easy. Some users have the AI draft the commit message, then they quickly edit or approve it and commit – it saves thinking of wording. ### **7.2 Best Practices for Prompting and Collaboration** Communicating with the AI effectively is an art. Here are some best practices when prompting Cursor’s AI, drawn from community experiences: - **Be clear and specific in instructions:** The AI isn’t a mind-reader; it responds to what you ask. When editing code via Composer or Agent, give explicit directives. For example, “Refactor this function” is okay, but “Refactor this function to use a dictionary for lookup instead of if-elif chain” is much better – it tells the AI exactly what you expect. Ambiguous prompts can lead to unexpected results, which might break your flow as you then have to correct them. - **Provide examples when possible:** If you want the AI to follow a certain pattern, show it an example. E.g., “Format the output like: Task 1: ” or “Here’s how another function is documented, do it similarly.” The AI learns from patterns; if you demonstrate one, it will mimic it. Cursor’s ability to reference code with @ means you can literally say “Make this function’s style match @other_function”. - **Use step-by-step prompts for complex tasks:** If what you want is complicated, consider guiding the AI through it instead of one giant leap. For instance, rather than “Build me a chess game engine” in one go (which might overwhelm it), you could break it down: “First, create classes for Piece and Board”, then “Now implement movement rules for each piece”, etc. This aligns with the iterative workflow and gives the AI manageable goals. - **Leverage the AI’s explanations:** Don’t just use the AI for code; use it for understanding. If you get code from the AI and you’re not 100% sure how it works, ask the AI to explain it to you: “Explain the above code line by line.” If the explanation reveals any issues or assumptions, you can address them. Similarly, if you’re stuck deciding how to approach something, discuss it with the AI: “Should I use approach X or Y here? What are the pros and cons?” The AI might lay out considerations (say, memory vs speed, or clarity vs conciseness) that help you decide. - **Maintain control over critical decisions:** While the AI can generate ideas, certain key decisions should still be yours – especially around architecture, security, and complex logic. Use the AI as input, but apply your judgment. For example, the AI might suggest using a certain library for convenience; double-check if that’s acceptable for your project (licensing, performance, etc.). Or if you’re implementing an algorithm where correctness is paramount (say a financial calculation), you might choose to write that yourself or thoroughly test the AI’s version. *You are the lead, the AI is your assistant.* - **Validate AI output from multiple angles:** A great habit is to use the AI to validate its own work. After writing a piece of code, ask something like, “Are there any edge cases this code doesn’t handle?” or “Can you write a quick test for this function?” This often flushes out things you or the AI didn’t consider initially. It’s like having a built-in code ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=,Careers))However, also do traditional validation: run the code, write your own tests, use external tools (linters, type checkers). AI is not a substitute for real execution and verification – sometimes it might *think* a piece of code is fine but at runtime it fails. ### **7.3 Time Management and Focus** One of the benefits of Cursor and vibe coding is raw speed – tasks that took days can sometimes be done in hours. But a known risk with any high-speed development (even with human pair-programming) is losing time on tangents. Here are tips to manage your time: - **Set goals for your session:** Before diving in, decide what you want to achieve in this coding session (e.g., “implement feature X” or “fix bug Y and write tests”). This helps avoid getting sidetracked by AI suggestions that, while interesting, are out of scope. The AI might spontaneously suggest additional features or clean-ups – note them down for later rather than derailing your current goal. - **Use the AI to avoid procrastination on tough tasks:** We all have parts of coding we procrastinate on (writing tests, documentation, etc.). Let the AI handle the boring bits to keep you moving. For instance, if you find writing tests tedious, delegate the first draft of tests to Cursor as we practiced. Then you just refine them. This prevents backlog of undone tasks. - **Avoid over-engineering via AI:** It’s tempting to ask the AI to generate very elaborate solutions (because it can), but stick to YAGNI (You Aren’t Gonna Need It) principles. If you prompt Cursor for a simple feature and it starts building a whole framework, intervene and rein it in. A real example: someone might ask for a logging utility and Cursor might pull in a complex logging config – if your project doesn’t need that complexity, prompt it to simplify: *“That’s too complex; simplify the logging to just use print for now.”* Remember, *you* define the vibe – if you want a lightweight solution, let the AI know. - **Take breaks and context resets:** Working with AI can be intense because it’s so interactive. Don’t forget to take short breaks to clear your head. Interestingly, you can even tell the AI you’re taking a break and when you come back, ask it to summarize what you both have done so far – a quick recap to refocus. If a chat session gets too muddled (long history causing confusion), you can use Cursor’s feature to start a **fresh chat** or **custom mode** to reset context and then provide a concise summary of where you left off. This can solve situations where the AI starts giv ([Cursor's Magic Comes with a Catch: The Trust Setting You're Missing](https://www.imperva.com/blog/cursors-magic-comes-with-a-catch-missing-trust-setting/#:~:text=Cursor%27s%20Magic%20Comes%20with%20a,enabled%E2%80%9D%20to%20true))nt or circular answers because the session history is convoluted. ### **7.4 Ensu ([Cursor's Magic Comes with a Catch: The Trust Setting You're Missing](https://www.imperva.com/blog/cursors-magic-comes-with-a-catch-missing-trust-setting/#:~:text=Missing%20www,enabled%E2%80%9D%20to%20true)) and Consistency** Best practices from traditional development still apply, with some new twists: - **Code Style Consistency:** Cursor’s suggestions should follow your project style, but if you notice inconsistencies (maybe because training data varies), you can enforce rules. Use a linter/formatter (like ESLint, Prettier, Black, etc.) as you normally would; or ask the AI to “Apply PEP8 style” to a Python file – it might do basic formatting fixes. Also, as mentioned, maintain a `.cursorrules` or similar to remind the AI of style preferences. For example, “Always use 4 spaces indentation” or “Use TypeScript type annotations on all functions” can be stipulated. - **Documentation and Comments:** It’s easy to forget docs when code is flowing out quickly. Leverage the AI to keep docs in sync. After implementing a function, use the chat: *“Write a docstring for the above function.”* This not only produces documentation but sometimes can reveal if the AI misunderstood the function (if the docstring is off, you know there’s confusion). Additionally, consider having the AI update any relevant documentation file when you change functionality: e.g. *“Update the README usage example for the add_task function.”* Little by little, this keeps your project docs up-to-date. - **Community and Knowledge Sharing:** If working in a team, you might wonder how vibe coding fits in. One approach is to treat AI outputs as drafts that still go through code review. You can commit AI-generated code and have human teammates review it – often it’s much quicker to review than to have written from scratch. Team members might have insights (like “this looks good but we usually avoid using library X for Y reasons”). Over time, teams may even develop shared prompt templates or rules for Cursor so that everyone’s AI assistance is aligned. For instance, a team might agree to a standard header comment format and have a stored prompt for Cursor to generate it. Because vibe coding is new, it’s good for teams to openly discuss how they’re using it and ensure it’s enhancing collaboration, not hindering it. - **Security and Sensitive Code:** If you’re working on open source or proprietary code, be mindful of what you share with the AI. Cursor’s Privacy Mode can be toggled if you don’t want code sent to the cloud – though note that may limit the AI’s capability (if using local models or none). For critical code (security, crypto, etc.), double-check AI outputs with extra scrutiny. The AI might not have malicious intent, but it could inadvertently introduce a vulnerability if you don’t specify constraints. Always validate with tests/static analysis especially in these areas. By following these practices, you can integrate vibe coding into professional-grade development. Many early adopters report that their workflow did change – they spend more time formulating what they need and reviewing AI output, and less time typing boilerplate or searching through documentation. This aligns with what IBM’s Shalini Harkar observed: developers can *“focus more on solving real-world complex problems... designing efficient ar ([What’s the vibe around vibe coding? | IBM](https://www.ibm.com/think/news/vibe-coding-devs#:~:text=Director%20Andrej%20Karpathy%20recently%20used,%E2%80%9D)).. and fostering innovation rather than routine tasks”*. In essence, you elevate your role to problem describer and solution architect, while the AI handles a lot of the routine implementation under your guidance. With a sound workflow and these best practices, you’re set to code both *fast* and *well* using Cursor. Next, we’ll look at some case studies and scenarios where vibe coding shines, to inspire you with what’s possible. ## 8. Case Studies & Use Cases Nothing illustrates the power of vibe coding better than real-world examples. In this section, we’ll explore a few case studies and use cases – some real, some hypothetical – to see how developers are applying Cursor and the vibe coding approach to projects. These stories range from individual hobby projects to team workflows and highlight both the successes and lessons learned. ### **Case Study 1: Solo Developer Builds Two Apps from Scratch** Meet Edu, a self-described backend developer with “no idea” about frontend development. Edu decided to test how far current AI tools could take him in building something outside his comfort zone. In a weekend experiment, he set out to create two simple web apps from scratch using vibe coding principles – essentially letting curiosity and AI guidance lead the way. The first app, *SimpleCalendar*, was a basic calendar widget. Edu started with just a general goal (“I want a simple calendar UI I can click through”) and used a prompt-generation tool followed by Cursor. He iterated in short sessions (~10 minutes each) a few times per day, gradually refining the UI. In early iterations, he relied on another tool to handle some image-based prompting (sending screenshots with annotations to an AI service) but once those credits ran out, he switched entirely to Cursor connected to GitHub. With Cursor’s agent, he continued development, and after only about 4–5 total hours of active work, he had a functional small app deployed. Notably, he didn’t write much code manually – he mostly guided the AI by describing desired changes and providing feedback on results, truly “vibing” with the process. For the second app, *eferro Picks* (a personal link/bookmark manager), Edu streamlined his approach further. This time he went straight to Cursor’s agent for most of the development, skipping the external prompt tool. The workflow became continuous: he would describe a change or feature in Cursor’s chat, the agent would implement it, and he would test and then describe the next change. He even used Cursor’s agent to explore data (likely reading/writing a JSON of links) and to ask for design advice on the fly. In about 5–6 hours, this second app was done. The key takeaways Edu shared: vibe coding allowed him to *learn by doing* in an unfamiliar domain, and the AI’s help meant his lack of frontend knowledge was not a blocker. However, he did constrain the problem to something non-critical (personal tools, no complex logic or multi-user concerns) – an important consideration he noted (vibe coding works best when the stakes are low and you can afford to experiment). This case shows an individual using Cursor to rapidly prototype and build projects that would have normally been daunting or time-consuming. It ([Cursor - The AI Code Editor](https://www.cursor.com/#:~:text=Blog%20Blog%20Iterating%20with%20Shadow,by%20Arvid%2019%20minutes%20read)) how vibe coding can empower a developer to step outside their specialization, relying on AI suggestions to handle the parts they don’t know. By keeping the projects small in scope, Edu could embrace the freedom of vibe coding without worrying about long-term maintenance or heavy-duty robustness. The result: two functional apps and a developer who gained new experience and confidence in just a couple of days. ### **Case Study 2: 8-Year-Old Codes a Game with Cursor** One often-cited promise of vibe coding is making programming more accessible to people who aren’t traditional developers. An anecdotal but compelling example: an **8-year-old** child was able to use Cursor to build a simple game, with minimal direct coding. This story, shared via social media, demonstrated that with an intuitive interface and AI assistance, even a young beginner could go from idea to working code. The child used Cursor’s chat to describe the game (a basic dice game) and followed the AI’s instructions to assemble it. According to the account, the child didn’t write code from scratch; instead they would say what they wanted (like “when I click the button, it should roll a dice and show the result”), and Cursor generated the code for that behavior, which the child then ran to see the outcome. One of Cursor’s early adopters, Ben, mentioned this example in context: *“Anyone is going to be able to code in some years thanks to Cursor. This 8 year old showing how easy it is... is just the beginning.”*. The implication is that vibe coding tools like Cursor lower the barrier to entry dramatically. Now, the game the child made was presumably very simple (perhaps a few lines or using a high-level library), and surely an adult or the AI guided them. But the fact it was possible at all is striking. We can imagine extending this to education: students could use vibe coding to create small apps or data visualizations as part of learning other subjects, without needing deep programming expertise – essentially *“programming by conversation.”* For a more concrete scenario, consider a beginner who knows the basics of Python and wants to make a text adventure game. Using vibe coding in Cursor, they can iterate by saying, “Create a room with a description and an item to pick up,” then “Add a player inventory and a command to take items,” etc. The AI handles the Python syntax and logic, while the beginner focuses on the story and rules of the game. With each step, they learn a bit (“oh, that’s how a list is used to store inventory”) without being overwhelmed. This democratization of coding is a powerful use case of vibe coding – it can bring more people into software creation by making the process more about *what you want to make* than about the arcane details of how to make it. ### **Case Study 3: Professional Developer Team Integrating Cursor** It’s not just solo experiments – professional teams are also exploring vibe coding. Consider a scenario with a small startup’s engineering team. They have an existing codebase (say a web app with a Node.js backend and React frontend). The CTO introduces Cursor to the team to see if it speeds up development of new features. One engineer, Alex, is tasked with adding a payment processing module. Normally, Alex would spend a lot of time reading API docs of Stripe or PayPal and writing boilerplate integration code. Using Cursor, Alex tries a vibe coding approach. He opens a new branch and then opens Cursor’s chat: *“I need to integrate Stripe payments. Create a new module to handle checkout, using Stripe’s API (charge a credit card token).”* In Agent mode, Cursor goes ahead and generates a `payments.js` with a basic Stripe integration (perhaps using the Stripe SDK). It might not compile right away if Alex hasn’t installed the `stripe` package – but Cursor is smart enough that it might include a note like “// Ensure to install stripe: npm install stripe” in a comment. Alex does that, then runs the code. He finds an error because he needs to provide an API key. He fixes that by adding a configuration and tells Cursor to use the config. The agent adjusts the code accordingly. Within an hour, Alex has a working payment flow that would have normally taken much longer. He writes some tests (with AI help) and commits the feature. In code review, his teammate reviews the changes. The teammate might be surprised at the speed, but they verify the logic. They catch a minor issue (perhaps error handling for declined cards is missing). Instead of manually writing the fix, the teammate decides to try Cursor’s suggestion capability: they highlight the relevant section in the PR diff and prompt Cursor (via the VS Code extension interface of Cursor) to add error handling. Cursor provides a patch, which they apply and push. The review passes. This hypothetical shows how a team might leverage Cursor not just individually but collaboratively. It also reveals some lessons: - Cursor can **accelerate integrating external APIs and libraries**, thanks to its training on a broad corpus (it likely “knows” common usage patterns for Stripe, etc., or can quickly fetch them with @Web). - Code reviews become about validating AI output and refining it, which can be faster than writing from scratch but still requires human insight (to catch domain-specific needs the AI missed). - There’s potential for every developer on the team to effectively have an AI pair programmer, which might level the playing field between senior and junior devs to some extent. A junior dev with Cursor can produce code that’s closer to what a mid-level dev might produce, because the AI infuses best practices (and the senior devs can focus on guiding those AI outputs to fit the codebase style). Another use case in a team context is using Cursor’s **MCP integration** for internal tools (something we’ll cover in section 11). For example, a company could create a custom MCP server that provides access to their internal database or microservices. A developer could then prompt Cursor, *“@CompanyDB query: get all users who signed up in the last week”* and the MCP integration would fetch that data, perhaps using it to seed test cases or populate the dev environment. This turns vibe coding into not just writing code, but orchestrating knowledge and operations in the development process. It’s early days for such integrations, but some teams are experimenting with tying Cursor into CI/CD pipelines, documentation wikis, and more via MCP. Imagine an AI-assisted workflow where you could ask in Cursor, “Deploy this feature to staging,” and via an MCP tool that interfaces with your CI system, it actually triggers a deployment (after running tests). These case studies show that vibe coding isn’t limited to toy examples; it can be applied to real-world dev tasks, and when done carefully, can reduce development time significantly while keeping quality high. ### **Use Case: Rapid Prototyping and Hackathons** Outside of routine work, Cursor and vibe coding excel in **rapid prototyping scenarios**. Hackathons or innovation sprints are a perfect example. In a 24-hour hackathon, every minute counts, and participants often need to build something demo-able fast. Teams using vibe coding tools could potentially build out functionality far quicker. For instance, a team of 3 at a hackathon decides to create a web app plus mobile app integration. They divide tasks, but each uses Cursor to implement features at lightning speed – one works on the backend API (whipping up endpoints with the help of AI, even generating documentation for them), another designs the web front-end (perhaps even using image-to-code by sketching a layout and letting AI generate the HTML/CSS), and the third works on a React Native mobile app (with AI suggesting lots of boilerplate and API call code). They could finish what is basically an MVP of a startup in a day. In fact, we see glimpses of this already: hackathon projects in 2023–2024 have leveraged GPT-based tools to generate code, but having an integrated environment like Cursor streamlines it further (no copy-pasting between ChatGPT and your IDE, it’s all in one place). One interesting use case reported was a developer who built an entire app without writing code directly, calling it *“vibe coding” as coined by Peter Yang*. Ben (the developer) initially prototyped a dice game using an AI (Claude) and manual steps, which took ~20 hours. When he retried the project with Cursor, he did it in ~15 hours entirely through the IDE’s AI and **“without making a single direct edit to the code”*. He would prompt and apply, prompt and apply, achieving a working app faster than his previous attempt. This pseudo-real case (shared on LinkedIn) highlights how prototyping with vibe coding can drastically cut down development time – though Ben did note it wasn’t as smooth as the hype sometimes suggests, and integrating everything still took effort. Nonetheless, by the hackathon demo time, what matters is he had a working app to show. ### **Use Case: Maintenance and Legacy Code** Another domain to consider is maintaining or updating legacy code. Many developers inherit old projects with sparse documentation. Vibe coding can assist in understanding and gradually modernizing such code. For instance, a developer might use Cursor’s chat to ask questions about a legacy function: *“Explain what this function does and what its inputs/outputs are.”* If the code isn’t too convoluted, the AI will summarize it, effectively acting like a always-available colleague who has read the code. Then the developer can ask, *“Refactor this to use modern best practices (e.g., replace callbacks with async/await).”* The AI can handle the mechanical parts of such refactoring while the developer verifies it doesn’t break behavior. Over time, an entire legacy module could be revamped piece by piece with AI help, saving a lot of manual search-and-replace and rote work. Similarly for bug fixing: a developer can paste an error log or stack trace into Cursor and have the AI point out likely causes in the code. Or even ask the AI to write a failing unit test that reproduces a reported bug (if the developer describes the bug), then generate a fix. This speeds up the typical debug cycle. These case studies and scenarios demonstrate the versatility of vibe coding with Cursor. From empowering novices and speeding up personal projects, to fitting into professional team development and tackling legacy challenges, the pattern is consistent: **describe what you need, let the AI do the heavy lifting, then refine.** The experiences also teach us that while vibe coding can achieve a lot, human developers are still crucial to guide the process, ensure correctness, and provide creative vision. In the next section, we’ll compare this approach and tool with other IDEs and methodologies to further clarify where Cursor stands out and where traditional methods might still hold an edge. ## 9. Comparisons with Other IDEs & Philosophies With the rise of AI-assisted development, many tools and approaches have emerged. How does Cursor IDE stack up against other solutions like GitHub Copilot, Sourcegraph Cody, Tabnine, or even traditional IDEs without AI? And beyond tools, how does the *philosophy* of vibe coding compare to classical methods of programming (think test-driven development, waterfall vs agile, etc.)? In this section, we’ll compare and contrast to give you a clearer picture of where Cursor and vibe coding fit in the landscapeContinuing from the previous section: **Comparison with Other AI Coding Tools:** Cursor isn’t the only game in town for AI-assisted development. **GitHub Copilot** is perhaps the most well-known AI code helper. Copilot integrates with editors like VS Code and suggests code completions (usually one line or a block at a time) as you type. In terms of experience, Copilot feels more lightweight – it’s like an enhanced autocomplete – whereas Cursor provides a fuller environment with chat and agent capabilities. One user who tried both said *“it’s how Copilot should feel. I’m completely off VSCode now [for Cursor]”*. The big difference is that Cursor offers a chat interface with memory of your codebase and the ability to apply changes across files, run commands, etc. Copilot has recently introduced a separate *Copilot Chat* pane (as part of GitHub’s extension pack) which can answer questions and suggest refactors, but it’s not as deeply integrated into the editing workflow as Cursor’s Agent. Copilot also doesn’t natively have Cursor’s **“shadow workspace”** safety – changes are applied directly in your code only when you accept them, but Copilot doesn’t sandbox multi-step edits like Cursor’s agent does. That said, Copilot is very polished for single-file suggestions and has a huge training base from public GitHub code, which sometimes gives it uncanny accuracy on boilerplate. **Tabnine and Codeium** are other AI completion tools. Like Copilot, they plug into your existing IDE. Tabnine (which launched back in 2019) uses AI to suggest code completions but was limited to smaller models; it doesn’t have a conversational interface or deep project awareness. Codeium, on the other hand, shifted to providing a free AI coding assistant that also has a chat mode and supports multi-line completions. It’s similar in concept to Copilot (the team even touts that Codeium has *700K+ users* as of 2024) but it focuses on privacy (offering fully on-prem deployment for companies) and cost (free for individuals). Compared to Cursor, both Tabnine and Codeium lack the all-in-one IDE approach. You could, for example, use Codeium’s VS Code extension to get a chat sidebar not unlike Cursor’s – but you wouldn’t have features like Cursor’s integrated tool use (MCP) or the special diff view for edits. Another emerging tool is **Sourcegraph Cody**, which is tailored for understanding and navigating large codebases. Cody can answer questions about your code and generate snippets, leveraging Sourcegraph’s code search engine. In practice, Cody overlaps with Cursor’s “Ask” mode: both can do codebase Q&A and explain code. If you already use Sourcegraph for searching your company’s monorepo, Cody is great for natural language queries on that. However, Cody isn’t an editor – it’s more of an assistant you call into your editor, and it won’t directly apply changes to your files. You might use Cody to find the relevant function and then use Copilot or manual coding to modify it. Cursor combines these steps: find code, edit code, and even run code, all with the same interface. We should also mention **Replit Ghostwriter** – an AI pair programmer in Replit’s online IDE. Ghostwriter provides code completion and a chat, quite similar to Cursor’s basic features, but in the cloud. Replit’s advantage is a seamless environment for quick prototyping (especially for beginners or when you want to avoid local setup). Ghostwriter can even generate entire files or projects from prompts. Its scope is somewhat like Cursor, but because it runs in the browser, it’s geared towards simpler projects or learning environments. Cursor, being a full desktop IDE, is more suitable for large professional projects where you have a local dev environment, multiple files, and possibly the need for offline privacy. Users have noted that Cursor feels more *“integrated and professional”* whereas Ghostwriter is great for quick hacks or learning. In terms of vibe, both encourage you to describe what you want in English and get code – but Cursor gives you more control and tooling for complex tasks (like running tests, integrating with Git, etc., right in the editor). **Cursor vs Traditional IDE (without AI):** Apart from AI, Cursor is built on VS Code’s foundation, so as an IDE it offers much of the same functionality (debugger, extensions, version control integration). The difference is you have the AI features always at your disposal. A traditional IDE might have code generation snippets or templates, but those are manually written and far less adaptive than an AI. For example, JetBrains IntelliJ can generate a getter/setter or even auto-correct simple issues, but it won’t spontaneously suggest how to implement a new feature. In a sense, Cursor = VS Code + “Copilot on steroids.” It feels familiar but supercharged. Developers coming from a traditional IDE often comment that using Cursor is like always having documentation, code examples, and a refactoring engine on hand via plain language. Where a traditional IDE relies on the programmer to decide all the refactorings and find references, Cursor’s AI can proactively do a lot of that heavy lifting. **Philosophy: Vibe Coding vs Traditional Methods:** Vibe coding as a philosophy is quite a break from some traditional programming methodologies. In traditional development, especially in critical systems, there’s an emphasis on **control, precision, and predictability** – you plan what code to write, you write tests first (in TDD), you implement to spec, and you ensure you understand every line. Vibe coding flips some of that: it values *flow, speed, and creativity*, accepting that the code may be partially written by an AI that treats your instructions as the truth. As Ars Technica put it, *“Instead of being about control and precision, vibe coding is all about surrendering to the flow”*. This doesn’t mean abandoning correctness – you still test and verify – but during the initial creation, you might tolerate the AI taking paths you wouldn’t have manually, as long as it works out in the end. For example, compare **Test-Driven Development (TDD)** vs vibe coding. In TDD, you write a failing test, then write code to pass it, and you build your system through these small verified steps. In vibe coding, you might do the opposite: ask the AI to write the code for a feature, then once it’s done, ask the AI (or use your own skills) to generate tests for it and debug any issues. The flow is reversed. Some practitioners have even tried a hybrid: they prompt the AI to generate both the tests and the code in tandem – effectively letting the AI practice TDD! This can work for well-defined problems (e.g., “Write tests for function X that cover these cases” then “Implement function X to make those tests pass”). However, vibe coding doesn’t inherently enforce writing tests first; it’s more freeform. So, in environments where TDD or strict quality gates are required, developers will need to consciously apply those with the help of AI (for instance, explicitly asking the AI for tests as part of the process). **Waterfall vs Agile vs Vibe:** Waterfall model (sequential design, build, test phases) is largely at odds with vibe coding. Vibe coding is highly iterative and exploratory – you often don’t know exactly what you’ll end up with until you go through the AI-fueled loops. In that sense, vibe coding aligns more with **Agile/Lean** methodologies which favor quick iteration and adapting as you go. Agile teams might find that vibe coding with Cursor compresses the length of each iteration; what used to be a sprint’s worth of coding might be done in a day, allowing more time for feedback and adjustment. On the flip side, agile practices like code reviews, pair programming, and retrospectives are still valuable – you might just review AI-written code instead of human-written, or pair a human with the AI agent. Speaking of **pair programming**: traditionally that means two developers at one keyboard, brainstorming and catching each other’s mistakes. With vibe coding, the AI becomes the second “pair.” Some developers find this liberating – you get suggestions without feeling shy about making mistakes in front of a human peer. The AI also never tires or loses focus, although it also doesn’t truly understand project context or design beyond what you’ve told it. In pair programming, two humans can have a high-level discussion about architecture; an AI partner usually won’t proactively suggest architectural shifts unless prompted. So you could say vibe coding covers the micro-level pair programming (the AI is great at writing or editing code live with you) but not the macro-level (deciding overall architecture, which still falls to human designers). As a result, many teams might use AI pair programming for implementation but still hold design meetings and whiteboard sessions among humans to set direction. **Code Quality and Maintainability:** Traditionalists may worry that code generated by AI could be harder to maintain, especially if developers treat it as a black box. Indeed, a common critique expressed on forums is: *“Vibe coding stops you from actually understanding your code. You have to remember, AI is your tool, don’t make it the other way around.”*. In practice, the onus is on the developer to ensure they understand and clean up AI-produced code. Techniques like having the AI explain its output, writing docs, and adding comments help. Interestingly, vibe coding can lead to **more documentation by default**, since asking the AI to generate docstrings or comments is easy (a task often neglected when coding manually). It can also enforce consistency – if you set style guidelines and use the AI to apply them project-wide, you might end up with a more uniform codebase than a team of varied humans would produce. On the other hand, a potential pitfall is if an AI suggests a solution that *works* but is overly complex or not idiomatic for your team, future maintainers might scratch their heads. Thus, comparisons come down to this trade-off: *traditional coding = slower, but you’re intimately aware of every decision*; *vibe coding = faster, but you must consciously stay in the loop of decision-making*. **Human Creativity vs AI Generativity:** Traditional coding philosophies put human intellect and creativity at the center – the code is an expression of the developer’s thought process. Vibe coding suggests a more blended creativity. You still come up with the ideas and overall structure, but you let the AI fill in blanks, and sometimes even inspire new ideas (it might propose a clever approach you hadn’t thought of). Some have likened this to moving from being the *“writer”* of code to the *“editor”* of AI-written code. This resonates with Andrej Karpathy’s notion that programming may become more about *“guiding AI”* and less about typing exact syntax – akin to how a product manager guides a team, a developer might guide an AI agent in the future. Karpathy humorously called this *“Software 2.0”* where neural networks do the heavy lifting. Vibe coding is a step in that direction: you could view it as Software 2.0 at the development stage (even if the final product is still regular code, not a neural net). In summary, when comparing vibe coding with Cursor to traditional methods, it’s clear each has its strengths. Vibe coding excels in **speed, exploration, and reducing grunt work**, potentially leading to happier developers who can focus on creative aspects. Traditional methods excel in **deliberate design, thorough understanding, and predictability**. Many teams will likely forge a hybrid path: using vibe coding in parts of the workflow (like prototyping, implementing non-critical components, documentation, tests generation) while still using traditional rigor for critical components (design reviews, manual audits for security-sensitive code, etc.). The key is treating AI as a powerful tool to be wielded, not an infallible magic box. ## 10. Customization, Extensions & Future Enhancements One of the great things about Cursor being based on VS Code is that it inherits a rich ecosystem of customization and extensions. You can tailor the IDE to your preferences, and even extend its capabilities beyond what comes out-of-the-box. In this section, we’ll look at how you can customize Cursor (themes, settings, etc.), make use of extensions (including those not specifically related to AI), and then gaze into the crystal ball at future enhancements and trends that might shape Cursor and vibe coding in the coming years. ### **10.1 Customizing Your Cursor IDE** If you’re particular about your development environment, fear not – Cursor can likely be bent to your will. It supports the majority of VS Code’s customization options: - **Themes and Color Schemes:** You can install any VS Code theme extension to change the editor’s appearance (whether you want the soothing blues of Solarized, the contrast of One Dark Pro, or a funky neon theme). The Cursor website notes that you can *“import all your extensions, themes, and keybindings in one click”*. In practice, you might sign into Cursor with your VS Code account or simply install themes from the Extensions panel. If you previously used VS Code with sync enabled, your theme might even come along automatically. A popular choice for vibe coding is to use a theme that differentiates AI-suggested text (some Cursor users tweak the color of ghost text to be subtle). Cursor currently shows AI suggestions in ghosted text until you accept them, similar to GitHub Copilot’s style, which helps distinguish what’s AI vs what you typed. - **Keybindings:** During first setup, Cursor lets you choose preset keybindings (VS Code default, Vim, etc.). If you’re used to say, Vim-style modal editing, you can enable Cursor’s Vim emulation extension and it should work (the AI features trigger on certain commands that remain accessible via leader keys). Likewise, if you have custom shortcuts (maybe you like Ctrl+Shift+L to do something specific), you can add those in the keybinding JSON just as in VS Code. The aim is for Cursor to *“feel familiar”* to you, so you don’t have to relearn editor muscle memory. - **Settings and Preferences:** All the usual VS Code settings are there. For example, you can configure font size, tab vs spaces, linting rules, and so on through the Settings UI or JSON file. Additionally, Cursor adds some AI-specific settings. For instance, you might find a setting for the default chat mode (Agent vs Ask), toggles for features like “Automatically apply small changes” or “Confirm before running tools.” In Cursor’s Settings under “Chat” or “AI,” you can adjust whether the Agent auto-runs code or requires a button press. There’s even a **“Privacy Mode”** toggle – enabling it ensures that your code is not sent to Cursor’s cloud servers for model processing. In Privacy Mode, Cursor will either use local models (if available) or limit functionality. This mode is useful for proprietary codebases where sending code to an external API is a concern (though note: it might degrade the AI’s performance since the most powerful models are cloud-based). - **Rules for AI / Config Files:** As mentioned earlier, you can create configuration files to guide the AI. The `.cursorc` or `.cursorrules` file is one way to do it – here you can write project-specific instructions. For example: *“Our code uses 4 spaces indentation, no semicolons (if JS), and all public functions must have JSDoc comments.”* Cursor will try to heed these if provided. Similarly, you could specify particular *naming conventions* or *architectural hints*. This is a form of customization that steers the AI’s behavior. It’s not foolproof, but it can significantly shape outputs. The ability to influence the AI with project rules is a unique kind of customization that old IDEs never had – you’re effectively configuring your coding assistant’s style and knowledge. ### **10.2 Using VS Code Extensions in Cursor** Because Cursor is a fork of VS Code, most VS Code extensions work seamlessly. This means you can add **language support** for obscure languages, **linters and formatters**, **Docker integration**, **GitLens** for advanced git blame visualization – whatever tools you relied on, likely have an extension. For instance: - If you code in Rust, installing the Rust Analyzer extension in Cursor will give you the same inline error checking and IntelliSense you’d expect. - If you want to use Vim keybindings, the VSCodeVim extension can be installed. - Themes, as discussed, and icon packs (like Material Icon Theme for file icons) all personalize your workspace. - **Live Share:** Microsoft’s Live Share extension, which enables collaborative editing, could theoretically be used in Cursor. Imagine pair programming where both people also have an AI assistant active – that’s a wild but possible scenario (though one might want to disable the AI to avoid confusion during a live session). One thing to note: Cursor’s team has to ensure compatibility as they modify the editor for AI features. In general, most extensions work, but once in a while an extension that relies on very specific VS Code internals might have an issue. The Cursor community forum is a good place to check if a certain extension is known to be problematic. So far, the approach has been that Cursor *inherits* rather than reinvents the extension API, so compatibility is high by design. **Extensions that Enhance AI capabilities:** There are even some extensions specifically meant to complement AI coding. For example, an extension like *CodeTour* (that lets you create guided walkthroughs of code) might pair nicely with Cursor – you could have the AI generate a CodeTour for new contributors. Or an extension that visualizes algorithms (like drawing a diagram of a parse tree) could be invoked by AI commands if integrated. As of now, these are creative combinations rather than out-of-the-box features. Another area is **custom model or API integration**. By default, Cursor uses the models and keys provided by Anysphere (the company). But under *Settings -> Models*, you might find options to plug in your own OpenAI API key or other model endpoints. This means if you have access to, say, OpenAI’s API, you could configure Cursor to use your API key (perhaps to access GPT-4 with a higher rate limit or a specialized model). There’s also support for Anthropic’s Claude via API keys in the settings. This kind of extension – swapping out the AI engine – is powerful. For instance, a company could run a local LLM on their server and configure all team’s Cursors to use that for privacy (though performance might vary). Cursor’s devs have signaled that they intend to remain model-agnostic where possible, effectively letting you choose the “brain” behind the IDE. ### **10.3 Anticipated Future Enhancements** The pace of improvement in AI code tools is rapid. What might Cursor IDE look like a year or two from now? Here are some educated guesses and speculations, based on current trends: - **Deeper Autonomy and Agents:** Cursor’s Agent mode is already an early form of an autonomous coding agent. We can expect these agents to become smarter and more independent. For example, future versions might handle higher-level tasks like “Add a shopping cart feature to my web app” by breaking it down into sub-tasks automatically (create database model, update frontend, etc.), essentially acting like a junior developer that can plan its work. We might see integration with planning algorithms so that the agent can manage more complex, multi-session objectives. Of course, with more autonomy comes the need for more control – so likely Cursor will introduce ways to constrain or review an agent’s plan before execution (maybe a UI that shows a draft plan for user approval). - **Improved Context Handling:** Current AI models have context length limits (though they’re growing – e.g., GPT-4 can handle 8K, 32K tokens in some versions, and research models even more). In the future, Cursor might integrate models that can effectively hold an entire codebase’s context at once, or use clever retrieval so that the AI rarely says “I can’t see that file.” Imagine an AI that has indexed your entire 100k lines project and can answer questions or make changes anywhere instantly. We’re heading there. This will make vibe coding even smoother, as the AI will rarely need you to explicitly provide context or open files – it will know where to look. Sourcegraph’s Cody and others are already pushing in this direction with vector databases; Cursor’s MCP memory tools like Graphiti (using Zep) are another approach. In short, expect the “knows your codebase” aspect to get supercharged. - **Tighter Integration with DevOps:** Right now, Cursor helps primarily in the coding phase. But future IDEs might integrate into the whole developer lifecycle. We might see Cursor tie into issue trackers (imagine the AI knows the issue you’re working on and can reference its description automatically), or into CI/CD (the AI might warn, “The tests are failing on CI, let me fix that” and open the pipeline logs for you). In fact, Cursor’s devs blog about things like using *“shadow workspaces”* and *“kernel-level folder proxies”* to let AIs iterate on code safely – perhaps in the future, the AI could even maintain a parallel branch of your repo where it continuously refactors or updates dependencies, and then proposes a pull request to you when ready. - **Voice and Multimodal Inputs:** It’s not far-fetched that you might talk to Cursor or draw diagrams for it in the future. Already one can drag images (like UI designs) into chat. Looking ahead, there could be a feature where you sketch a flowchart of an algorithm and the AI generates code from it. Or you say, “Hey Cursor, create a new React component that looks like this [pointing to a design file].” Some developers experiment with voice coding via tools like GitHub Copilot Voice (a research project). If Cursor added voice recognition, vibe coding could literally become speaking out loud while the IDE writes code – truly coding at the “speed of thought and speech.” This could open programming to those who prefer verbal communication or have difficulties with typing. - **Extension of Vibe Coding to Other Domains:** While our focus is code, the same philosophy might extend to configuration, infrastructure as code, etc. Cursor might evolve to assist not only with app code but also with writing a Kubernetes config, or designing a database schema by example. For instance, *“Set up a Postgres DB and generate a Docker compose file for it”* – the AI could just do that. It already can, but future versions will be even more adept at multi-file, multi-technology tasks. The lines between code, config, documentation, and even design might blur, with a single AI assistant helping across all. - **Collaboration and Sharing of AI Knowledge:** As more people use vibe coding, communities might share prompt techniques or even AI configurations as extensions. Perhaps a future Cursor enhancement is a **Prompt Library** built-in, where you can one-click apply a recipe like “Optimize this code for speed” or “Implement the Builder design pattern here” – essentially curated prompt macros. Developers might publish these recipes (somewhat like VS Code snippets but powered by AI). Cursor could also incorporate feedback mechanisms: e.g., if an AI suggestion is incorrect and the user fixes it, the system might learn from that. This edges into the territory of training custom models on community data. If handled well (and privately opt-in), that could continuously improve the AI’s capabilities on real-world code. - **Larger Language Model Integration:** The tech world expects even more capable language models (GPT-5? new open-source models?) to come. Cursor will likely integrate the best available. A newer model might deeply understand not just code syntax but also the intent and semantics. It could potentially do things like formally verify certain properties, or instantly suggest multiple alternative implementations (letting you pick by style or efficiency). It might also reduce current limitations – today, sometimes the AI might time-out or give up on a complex problem. Future models might persistently work on a hard problem and solve it (imagine an AI that can actually write a novel complex algorithm, not just mimic ones it’s seen). That would widen the scope of tasks you’d trust the AI with. In terms of Cursor’s own future enhancements beyond the AI itself, they’ve been raising substantial funds (OpenAI’s CEO invested; they had a \$60M Series A and more funding in 2024). This suggests they will remain aggressive in adding features. One likely path is making Cursor more **enterprise-friendly**: better offline support, on-prem deployment of the AI server for companies, robust security features (for example, an admin console to restrict what data the AI can access). They might also integrate with other tools developers love – perhaps a built-in GUI for git commit history (with AI explanations of changes), or connecting with design tools like Figma (so the AI can fetch design specs). **In a nutshell**, the future of Cursor and vibe coding looks bright and fast-evolving. Customizing your IDE experience is already easy with themes and extensions, and it will get even richer as AI becomes more integrated. We foresee a world where the IDE not only helps you write code, but becomes a co-developer that participates in planning, coding, debugging, and even deployment. Cursor is among the pioneers of that paradigm, and by using it today you’re getting a glimpse of what everyday coding might look like for everyone in a few years. Stay tuned, keep your IDE updated, and don’t be surprised when new features drop that feel almost like sci-fi – because those may be just around the corner. ## 11. MCP Integration (Model Context Protocol) One of Cursor’s most powerful advanced features is its support for **MCP (Model Context Protocol)**, which allows the IDE to interface with external tools, systems, and custom logic in a standardized way. In simple terms, MCP is like a **plugin system for the AI** – it lets you extend what the AI assistant can do by hooking into other programs or APIs. If you’ve heard of ChatGPT “plugins,” this is a similar concept: giving the AI new abilities by connecting it to something else. In this section, we’ll give an overview of MCP, discuss its architecture, walk through how to set it up in Cursor, explore hands-on examples, and consider why MCP is a big deal for the future of vibe coding. ### **11.1 What is MCP and Why Use It?** MCP stands for **Model Context Protocol**. It’s an open protocol (with its own documentation at modelcontextprotocol.io) that standardizes how an application like Cursor can provide additional context or tools to the language model. Think of MCP as a way to teach the AI new tricks. Out of the box, Cursor’s AI can read and write your code, but with MCP you could, for example, let the AI query a database, call an API, or retrieve information from your company’s internal knowledge base – all during a chat or agent session. Why is this powerful? Because it breaks the AI out of its sandbox. Normally, an AI confined to the IDE only knows what’s in your files and what you tell it. With MCP, the AI could answer questions like *“What’s the top StackOverflow answer for this error code?”* by actually searching the web (if a web search MCP tool is connected). Or it could fulfill a command like *“Create a Jira ticket for this bug”* by using a Jira integration tool. Essentially, MCP lets Cursor act on your behalf in external systems, automating not just coding tasks but meta-development tasks. Some practical use cases the Cursor docs and community have mentioned include: - **Database queries:** Instead of manually providing a schema to the AI, hook up an MCP server that the AI can ask, “Hey, what’s the schema of table X?” or “Insert these sample records.” The AI can then use actual database results in its context. - **DevOps/CI tasks:** Imagine the AI can run your test suite or build process via MCP. Then, if tests fail, it knows and can try to fix the code accordingly. A user on YouTube demonstrated using MCP to integrate with Zapier, enabling Cursor to trigger CI jobs and other web services. - **Memory/Knowledge:** One of the popular MCP tools is **Graphiti** using Zep: it provides long-term memory. It can store and retrieve information like “What decisions have I made previously about architecture?” so the AI doesn’t forget conversation context over long sessions. This persistent memory can be project-specific preferences or even your personal coding style. - **External APIs (Slack, Jira, GitHub):** As hinted in an earlier example, you can integrate with productivity tools. For instance, an MCP server could interface with **GitHub** – then the AI could create a pull request, fetch code from another repo, or comment on an issue. Another could interface with **Slack** – enabling the AI to send a message to your team (with your approval) summarizing what it did. In essence, MCP is about turning Cursor from just an editor into a development assistant that spans your whole ecosystem. ### **11.2 MCP Architecture & How It Works** Under the hood, MCP works by running separate processes (called **MCP servers**) that communicate with Cursor’s AI agent through a defined protocol. There are two types of communication supported: **stdio** and **SSE (Server-Sent Events)**. - **stdio Transport:** This means the MCP server is a program that runs locally and communicates via standard input/output streams. Cursor can launch this program automatically when needed. It’s simple: Cursor sends it a request (e.g., `{"command": "query_db", "params": {...}}` in JSON format, presumably), the program does something and prints the result back as JSON, which Cursor then feeds into the AI’s context. stdio servers are easy to set up – you can write them in any language (Python, Node, etc.) that can read from stdin and write to stdout. The nice part is these servers run on your machine, so they can access local resources safely and aren’t exposed externally unless you choose. - **SSE Transport:** This is a more web-like approach. The MCP server runs as a web service (local or remote) and exposes an SSE endpoint (essentially a streaming API). Cursor will connect to that endpoint to send requests and receive responses/events. SSE servers are useful if the tool is remote or shared by multiple users. For example, a company might host a single SSE-based MCP server that connects to a proprietary system (like a big internal knowledge base) and all developers’ Cursors talk to it. SSE allows the responses to stream, which is handy for long-running operations or continuous updates. Cursor’s architecture treats these MCP servers as additional “tools” the AI can use. In the chat interface, when certain MCP tools are available, the agent might say something like: *“(Agent wants to use Tool XYZ with arguments ... )”*, and ask for your approval. Once approved, it calls the tool via MCP, gets the result, and continues. If you trust a tool fully, you can enable **Yolo mode** which auto-approves tool usage. For example, if you have an MCP server that runs tests, you might allow the agent to run it freely whenever it wants to verify the code. From a **configuration perspective**, adding MCP servers to Cursor involves editing a JSON config file. Cursor looks for `mcp.json` either globally (`~/.cursor/mcp.json`) or per-project (`.cursor/mcp.json` in your repo). In that file, you list each server with a name and either a launch command (for stdio) or a URL (for SSE). You can also specify environment variables in the config (for example, to pass API keys or configs to your MCP server process). Here’s a simplified example of what a project’s `mcp.json` might look like: ```json { "mcpServers": { "jira-helper": { "command": "npx", "args": ["-y", "mcp-jira-tool"], "env": { "JIRA_API_TOKEN": "your_token_here" } }, "db-tool": { "command": "python", "args": ["tools/db_tool.py"] }, "websearch": { "url": "http://localhost:8000/sse" } } } ``` In this hypothetical config, we define three MCP servers: - `jira-helper` uses a Node tool (maybe installed via npm) to interface with Jira, passing in an API token via env var. - `db-tool` uses a local Python script for database queries. - `websearch` connects to an SSE server (perhaps one that wraps a search engine) running on localhost:8000. Once this config is in place, Cursor will list these tools in the UI (likely under *Settings -> Tools* showing available MCP tools). The **AI agent automatically becomes aware of them** and can use them if it decides they’re relevant. For instance, if you ask “Find all TODO comments in the repo,” and you have a websearch or grep MCP tool, the agent might choose to invoke that tool to search your files. Security is an important aspect: by default, Cursor does not let the AI call arbitrary system commands without permission. MCP gives a controlled way to expose functionality. Each MCP server has only the access you grant it. For example, the `db-tool.py` might have read-only DB credentials, so even if the AI misuses it, it can’t drop your tables (in theory). Always consider the scope of what you enable. The good thing is you have to set these up explicitly – the AI won’t, for example, decide on its own to install an extension that deletes files. MCP servers do what they’re coded to do, no more. ### **11.3 Setting Up MCP in Cursor: A Quick Guide** Let’s do a quick hands-on imaginary setup to illustrate the process. Suppose we want Cursor to have access to an external knowledge base – maybe a company Wiki – so that it can answer questions like “What is the internal code style guide?” or “Link me the design doc for Project X.” We’ll create a simple MCP server for that. **Step 1: Write the MCP Server**. We decide to make a Python script that, given a query, returns a best-match Wiki page excerpt. We’ll call it `wiki_tool.py`. It might use an API or just search a local directory of markdown files. The key is it reads JSON from stdin and writes JSON to stdout. e.g.: ```python import json, sys # pseudo-code for reading one request from stdin for line in sys.stdin: req = json.loads(line) query = req.get("query") if not query: continue # do something with query, e.g., search files result_text = search_wiki_for(query) response = {"tool": "wiki", "answer": result_text[:500]} # return snippet print(json.dumps(response)) sys.stdout.flush() ``` This is a very basic loop that reads one query, responds with an answer. A more robust server might handle multiple commands or maintain state, but this will do. **Step 2: Register it in `mcp.json`.** In our project’s `.cursor/mcp.json`, we add: ```json { "mcpServers": { "company-wiki": { "command": "python", "args": ["tools/wiki_tool.py"] } } } ``` We place `wiki_tool.py` in a `tools` subfolder (just to keep our project tidy). Now, when Cursor starts in this project, it sees this config and will launch `python tools/wiki_tool.py` as needed. **Step 3: Use it in Cursor.** When we ask the AI something that should trigger it – e.g., “What is our code style guide?” – the agent might decide to use the `company-wiki` tool. The conversation might look like: AI: *“I found a relevant entry in the company wiki. Summarizing below:”* and then it provides the summary. Behind the scenes, what happened is the agent saw the question, recognized via its prompt that it has a tool `wiki` available (tools are described to the agent, probably like “Use @wiki to search the company knowledge base”), and it issued a request. Cursor’s interface may or may not show this explicitly. If set to manual approval, you’d see something like *“Agent requests to use Company-Wiki tool with query ‘code style guide’ – [Allow] [Deny]”*. Upon allowing, the MCP server ran, returned the snippet of the style guide, and the AI continued with that information. This interplay is slick – as a user, you might not even realize when the AI seamlessly pulls data via MCP versus “knowing” it from context, except that it can suddenly answer things it normally wouldn’t know. **Troubleshooting Setup:** If the MCP server fails to start or crashes, Cursor typically will show an error in the chat or in the developer console logs. A common mistake is forgetting to flush stdout (the AI waits and times out). Or if using SSE, dealing with CORS or network issues if it’s remote. The docs suggest using `cursor --disable-extensions` or checking logs if an MCP isn’t working. Community forums (and the Medium quickstart guides) provide examples and solutions for setting up common tools. ### **11.4 Hands-On Example: Extending Cursor with MCP** To illustrate with a concrete example from a user’s perspective: Let’s replicate (in a summarized form) what one Medium blogger did – integrating Cursor with Jira using MCP. **Scenario:** You often find yourself leaving the editor to update Jira tickets with the status of tasks or to add comments that you completed something. This context switch is a flow-breaker. With MCP, you decide to let Cursor do it for you. - You create a Node.js MCP server (using the official `@modelcontextprotocol/sdk` for Node perhaps) that has functions like `findTicket(id)`, `addComment(ticketId, text)`, etc., communicating with Jira’s REST API. - In the `mcp.json`, you register it as `"jira": { "command": "node", "args": ["jira_mcp_server.js"], "env": { "JIRA_TOKEN": "abc123" } }`. - Now, in Cursor, you might issue a prompt: *“Jira update: mark issue PROJECT-123 as done with comment 'Fixed in commit abcdef'.”* The AI interprets this and uses the Jira MCP tool to execute the actions. It might respond back in chat, *“Okay, I’ve updated the Jira ticket PROJECT-123 as Done and added the comment.”* Perhaps it even retrieves the latest status to confirm. This hands-on use saves you from alt-tabbing to the browser, navigating Jira, writing the comment manually. It’s all done in one go. In the Medium guide, the author outlined how to create such a tool step by step (with code to authenticate to Jira, etc.), and found it was doable within a short time. The ROI is that now any developer in the team can use the same functionality, or you could even share the MCP server (since it’s just Node, others can run it with their creds). **Another simple example:** Someone in the community made a **“calculator” MCP tool** – because sometimes you might want to do heavy calculations or use a specific library (like a big number math library) that the AI alone would struggle with accurately. The MCP server could take an expression and return a precise result. So if you ask in Cursor, “What’s the eigenvalues of this matrix (some matrix)?”, the AI could hand it off to a Python MCP tool running NumPy, get the answer, and format it back to you. That’s a trivial case where AI plus tool beats AI alone. ### **11.5 Why MCP Points to the Future** MCP integration in Cursor is more than just a nifty feature – it’s a glimpse at the **future of coding assistants as extensible platforms**. It acknowledges that: 1. No single AI model will have all knowledge or capabilities built-in (at least not yet), but we can composite capabilities. 2. Developers have unique environments – hooking into those (CI systems, custom scripts) is key to truly automate workflows. 3. It keeps the human in control by making tool use explicit and configurable. Looking forward, Model Context Protocol or similar ideas might become standard. Perhaps other IDEs will adopt the same protocol, so an MCP server you write could be used by multiple AI tools (the fact it’s open and documented encourages this). We might see an ecosystem of MCP servers – open-source ones for common tasks (like a “Google Search” MCP, a “StackOverflow QA” MCP, etc.). In fact, one search result hints at a **Cursor Directory** of MCP servers, implying a listing of community-contributed servers exists or is planned. There’s also a **PulseMCP** and others trying to build tooling around this. This open ecosystem could accelerate what AI assistants can do. It’s akin to how VS Code’s extension marketplace grew the editor’s powers in all directions – now, MCP servers could do the same for AI’s powers. From a user perspective, MCP integration means your Cursor IDE can continuously evolve to meet your needs. If tomorrow you start using a new technology or service, you can wire it in. If you need more memory for context, you can plug in a vector database via MCP. It’s a very *Unix philosophy* approach – have small tools that do one thing well, and wire them together (here, the AI agent is orchestrating these tools as needed, like a conductor). One should be aware though: with great power comes responsibility. An MCP tool that executes shell commands, for instance, could be dangerous if misused. Always keep an eye on what the AI is doing with tools. That’s why the default is to ask for approval. Over time, trust can be built (maybe you flip to auto-approve once you trust the combo of Cursor + a particular tool). In conclusion for MCP: **it extends vibe coding beyond just writing code to controlling the whole environment.** Cursor with MCP can potentially write code, run it, test it, document it, commit it, notify your team, and update your task tracker *in one continuous flow*. We’ve essentially introduced automation at each step where a human would normally have to stop coding and do something else. That’s an incredibly powerful notion – it moves us closer to the dream of higher-level programming where you focus on goals and the tools handle the rest. MCP is still early (some assembly required, as we saw in examples), but it’s one of Cursor’s most promising features for those willing to customize their AI assistant’s skill set. If you like tinkering and integrating, MCP will feel like a playground where you can augment your AI buddy with new “superpowers” almost at will. ## 12. Troubleshooting, FAQs & Developer Support As with any cutting-edge tool, using Cursor IDE and vibe coding techniques can sometimes lead to hiccups or confusion. This section compiles common issues and questions users have, along with tips for troubleshooting and getting help. Think of it as your go-to FAQ and support guide – so if you encounter a problem, chances are it’s addressed below. ### **12.1 Common Issues and Solutions** **Cursor isn’t responding / AI outputs are blank or cut off:** If you find the AI stops mid-sentence or doesn’t respond at all, it could be a few things. First, check your internet connection – even if you’re coding locally, the AI might be hitting a cloud model. If your connection blips, the AI might not get a response from the server. Cursor will usually indicate if that’s the case (maybe a little “disconnected” icon). Another culprit is model rate limits – on a free plan especially, you might exhaust your quota (Cursor might then queue requests or switch to a lesser model). In such cases, the app should notify you (perhaps in a popup or in the chat itself: “You have reached the free limit”). The fix is either waiting (for rate limit reset), upgrading your plan, or providing a personal API key if possible. If outputs are consistently cut off (like the AI never finishes a long explanation or code), that might be due to token limits of the model. A workaround: ask it to continue (“please continue”) or break your request into smaller parts. Newer models and versions of Cursor mitigate this with better streaming, but it can still happen on very large file generations. For now, you might also tweak the max response length in settings if available. **High CPU or Memory Usage:** Some users notice that Cursor can consume a lot of resources, especially when indexing a large codebase or running the AI on long tasks. If your machine’s fans spin up and things slow down: - Make sure you’re not running heavy processes in the integrated terminal inadvertently. - Check if the issue is an extension: run `cursor --disable-extensions` to see if performance improves. Sometimes a non-AI extension (like one parsing a huge file) could hog resources. - Use Cursor’s Process Explorer (Command Palette -> “Developer: Open Process Explorer”) to see what’s eating CPU. For example, you might find the `extensionHost` (where extensions run) is high – pointing to an extension issue, or the `ptyHost` (terminal) is high – maybe you have a runaway process. If it’s the main `renderer` process, it could be the indexing or just the UI struggling with a huge file. Consider excluding large generated files from the AI’s purview (via `.cursorignore` similar to `.gitignore`) so it doesn’t try to read megabytes of minified code. - On Mac, if you see a “Memory usage” warning that looks wild (like claiming hundreds of GB), note that Cursor had a known Mac bug with misreported memory usage. The advice is to double-check via Activity Monitor to get the real usage. Often it’s much less scary than the warning suggests. **The AI did something unintended in my code:** Perhaps you accepted an agent suggestion and it refactored more than you bargained for, or changed logic incorrectly. If you notice immediately, use Undo (Ctrl+Z) – Cursor tracks AI edits just like normal edits, so you can step back. If you only realize later, that’s when version control saves the day. Do a `git diff` to see what changed in that commit and revert portions as needed. To avoid this scenario, try to **review diffs** before applying (Cursor shows a diff view for inline edits – take a moment to scan it). And when in Agent mode, prefer to operate on one self-contained task at a time. If the agent changes something unrelated, you can type “don’t change X” or hit undo for that part and instruct it again. **AI keeps giving wrong/inadequate answers:** If you find the AI stuck in a loop or misunderstanding your question: - Ensure you provided enough context. For example, if you ask “optimize this function” without showing the function, the AI might flail. Use the `@` context feature to include the code in the prompt. - Rephrase your question. Sometimes one phrasing doesn’t click, but another does (e.g., “Why is this code slow?” vs “Profile this code for performance issues” might yield different answers). - If the AI is hallucinating (making stuff up), be more concrete: say “In file X on line Y we do Z, but…” – anchor it in something real. Also, verify if it’s using outdated knowledge (the model might be unaware of the latest library changes if not fine-tuned on them). - As a last resort, try switching the model (if on GPT-4, maybe try Claude or vice versa). Occasionally, one model might have better domain knowledge for your problem. **“Network Error” or “HTTP/2 not supported” issues:** Some corporate networks and VPNs block or interfere with the protocols Cursor uses to stream AI responses (HTTP/2 and SSE). The docs mention that if your environment doesn’t support HTTP/2, the AI features may fail to work. Cursor has an option to **disable HTTP/2** and fall back to HTTP/1.1 (slower but more compatible). On Windows, also ensure your SSL certificates are up to date (as connecting to cloud model endpoints might fail if your system’s certs are old or if behind a proxy requiring its own cert trusts). If you’re behind a strict proxy, you might need to configure proxy settings for Cursor (like environment variables `HTTPS_PROXY`). Check the “Common Issues” in Cursor docs if you suspect network woes – they explicitly address HTTP/2 problems and the fallback. **Installation or Update Problems:** If Cursor won’t install or launch: - On Windows, ensure antivirus didn’t block it. Some corporate antivirus might sandbox unknown apps. - On Linux, if the appimage or deb isn’t launching, run from terminal to see error output (could be missing library dependencies – though the AppImage should bundle most). - If updates aren’t applying (e.g., you see on Twitter a new version released but your app is still old), note that Cursor does staged rollouts. You might simply get the update a few days later. You can manually download the latest from cursor.com if impatient. - If you used an old version of Cursor and it expired (maybe a beta that had an expiration), upgrade to a current release from the website. **MCP Troubles:** We introduced MCP’s power, but it can be tricky. Common MCP issues: - The AI says it can’t see the tool or “I don’t have access to X.” This could mean your `mcp.json` isn’t being read (ensure it’s in `.cursor/` and named correctly, not `mcp.json.txt` by accident). Also check the syntax (a stray comma could invalidate it). - The AI tries the tool but it errors out. If a tool fails, Cursor should surface the error (often as part of the AI response or in a log panel). Debug your MCP server by running it standalone with test input to ensure it outputs proper JSON. - The AI never uses the tool even though it’s available. It might not realize it should. Tools have descriptions; if you didn’t provide one, the agent might not know when to use it. In Cursor’s config you can often specify a human-readable description for the tool’s purpose. Make sure that’s clear so the AI will invoke it at the right time. For instance, name it “WebSearch” with description “search the web for a query” – then if you ask a question it can’t answer from code context, it knows WebSearch is an option. For more MCP-specific troubleshooting, see if Cursor’s docs have a “Troubleshooting MCP” section or check community forums where many share their server configs and issues. ### **12.2 Frequently Asked Questions (FAQs)** **Q1: Do I need to be online to use Cursor IDE?** A: For the AI features, generally yes – at least for now. Cursor’s intelligent completions, chat, and agent are powered by models that, in the default setup, run on cloud servers. If you’re completely offline, Cursor will still function as a regular editor (it’s essentially VS Code) but the AI won’t respond. There is work towards offline models – for example, if you have your own local LLM and hook it via MCP or API keys, you could get some AI help without internet. But a typical installation of Cursor uses cloud APIs. The **Privacy Mode** ensures code isn’t stored server-side, but it still sends prompts and gets completions live. So, treat it like you would treat an online service. **Q2: Is my code safe? Will it be used to train AI?** A: The Cursor team has stated that if Privacy Mode is on, your code is not stored remotely. Even with it off, they likely use it only transiently to generate a result. They have SOC 2 certification for security. As for training, OpenAI and others have terms that they don’t train on your usage data by default (OpenAI has an API policy that data won’t be used to improve models unless you opt-in). We cannot speak for Cursor’s internal model usage, but given Privacy mode and the enterprise focus, they likely do not feed your proprietary code into public model training. It might, however, be used to improve their service in aggregate (for instance, learning that a suggestion was accepted or rejected to tune the system). If you’re at a company, check any agreements – some enterprises get custom instances. For personal use, exercising standard caution (not pasting your production secrets, etc.) is wise but overall, using Cursor should be as safe as using, say, GitHub Copilot (which millions do in private repos). If in doubt, use Privacy Mode and/or your own API keys pointing to providers with clear privacy policies. **Q3: Can I use Cursor for any programming language?** A: Cursor supports any language that VS Code does, which is practically everything. The AI model itself has been trained mostly on popular languages (Python, JavaScript, TypeScript, Java, C#, etc.), so it’s extremely good at those. It also knows a lot of C, C++, Go, Rust, Ruby, PHP, etc. For more niche languages, it can still help but quality may vary. You’ll also want to install any language server extension for syntax and errors. The AI can even handle markup like HTML, CSS, markdown, and configuration files (Dockerfile, YAML) pretty well, because those appear in its training data. In short: Yes, you can use Cursor for (almost) any language. If you hit one where it’s struggling (say a very new language or proprietary DSL), treat it like teaching a junior dev – you may need to give it more hints or examples. **Q4: How do I revert or undo changes the AI made?** A: Use standard Undo (Ctrl+Z / Cmd+Z) immediately if it’s recent. If not, use your version control. Cursor encourages frequent commits (and even can generate commit messages) – leverage that. If an AI change breaks something, you can always revert that commit or cherry-pick around it. Another tip: you can ask the AI to undo its own change. For example, “Actually revert the last change” – sometimes it will then put things back as they were (it has the context of prior code from the conversation). This doesn’t always work perfectly, but it’s worth a try for small reverts. **Q5: The tab completion is distracting / I prefer inline suggestions off sometimes. Can I adjust that?** A: Yes, you can adjust how aggressive the inline suggestions are. In Settings, look for something like “Inline Suggestions” or “Tab Completion”. You could turn off automatic ghost text and instead manually trigger completions with a shortcut (some people do this if they find the constant gray text annoying). Also, Cursor might have a hotkey to accept suggestions beyond Tab (like `Ctrl+Enter` or something for multi-line). If you want a more chat-driven experience and less inline, you could even disable inline completions and use Chat/Agent exclusively. It’s all about preference. During writing-focused times, some users toggle the AI off for fewer distractions, then turn it on when needed (perhaps via the “AI: Enable/Disable” command). **Q6: How does Cursor handle sensitive data in code (keys, passwords)?** A: Cursor’s AI has no built-in filter that blanks out keys in your code or anything – it sees your files as they are. However, reputable AI providers have policies to not intentionally output sensitive info verbatim unless contextually needed. So if you ask “What is my AWS key?” it likely won’t just spit it out (and please don’t ask the AI that!). Treat the AI as you would a human collaborator: don’t give it unnecessary sensitive data. If you have secrets in .env files, add those to `.cursorignore` (so it doesn’t index them). If the AI does happen to see a secret (say it’s in a config file you asked it to refactor), that data is part of the prompt to the model. Use Privacy mode if that concerns you, which will route to a local or privacy-respecting mode of operation. In any case, standard security hygiene applies: consider rotating keys that might have been exposed just to be safe, and avoid exposing them in the first place by using environment variable stubs or placeholders when working with AI. **Q7: I’m not getting great results – any tips to improve the AI’s answers?** A: Absolutely. This is partly a skill: - Be clear and specific in your prompts. Saying “help me” is too vague; say “help me optimize the sorting algorithm in function sort_array (shown above).” - Provide examples: If you want a certain style, show a short example and ask it to follow that style. The AI is pattern-based: give it patterns. - Use the **“Rules for AI”** feature: If you always want something (like always include comments, or never use var, only let/const), set that rule so you don’t have to repeat yourself. - Break complex tasks into steps and use the agent incrementally (maybe you’re trying to do too much in one go). - And leverage the fact you can talk to it: if it gives an answer that’s not useful, tell it specifically why (“That’s not correct because X. Instead, do Y.”). The models are trained to incorporate your feedback and adjust the answer. **Q8: Where can I get help or report issues?** A: The Cursor community is growing. You have several support avenues: - **Official Documentation:** Start there for guides and known issues (docs.cursor.com has sections we cited on common issues and troubleshooting). - **Cursor Forum** (forum.cursor.com): There’s a community forum where you can ask questions, share tips, and often the Cursor developers or power users will answer. It’s a good place to see if others have encountered your problem. - **GitHub or Issue Tracker:** If Cursor has an open-source component or public issue tracker, you can file bug reports. Given Cursor’s closed-source nature, they might use an in-house tracker, so the forum or a support email might be the route. Some companies also use GitHub issues for feedback even if the core isn’t open – check the website for a “Report Issue” link. - **Discord or Slack Communities:** Not sure if Cursor runs an official Discord, but many AI dev tools have one. A quick web search or checking their website will tell. Real-time chat with other users can be super helpful for niche questions. - **Social Media & Blogs:** Cursor’s team posts on their blog (accessible via cursor.com/blog) with updates and advanced topics. They also pay attention to Twitter (or X) mentions. Sometimes tweeting a question or reading others’ tweets can yield answers (the devs might respond or someone else who had that issue). - **Latent Space Podcast / YC Demo Day**: Cursor was first revealed on a podcast and YC demo; while that’s not support, it’s interesting content. More importantly, it means the company is engaged with the dev community – you might find their founders on Hacker News, etc., addressing concerns or answering Q&As about Cursor’s vision and usage. **Q9: How do I ensure the AI doesn’t do something destructive like delete code?** A: The AI will not randomly delete large swaths of code unless it’s in response to a command (like you asked to refactor or remove something). And even then, you have the diff review step. However, to feel extra safe: - Use version control (cannot emphasize this enough – it’s your safety net). - Run the agent in “dry run” mode if needed: you could copy files and test the changes there, or explicitly instruct “show me what you’d change without applying” – sometimes the AI can simulate the diff in the chat first. - Keep scope limited: apply changes file by file or function by function. Don’t say “refactor the entire project” in one go (the agent likely wouldn’t attempt that anyway without more focus). - Turn off Yolo mode (auto tool execution) if you want to review every step. - There’s also **Workspace Trust** settings (Cursor inherits VS Code’s concept of trusted workspaces). If you open an unknown project, VS Code usually restricts auto-running of tasks/extensions until you mark it trusted. Cursor’s docs mention Workspace Trust being disabled by default for convenience. If you prefer, enable it so that running any code (including possibly AI-run tasks) requires your consent in new workspaces. **Q10: Can I use Cursor and Copilot together?** A: Technically, yes, since Cursor can install VS Code extensions, you could even install GitHub Copilot extension inside it. But there’s little reason to – Cursor’s own AI features cover that territory, and running two AI suggesters might cause dueling outputs or more confusion than benefit. Some users do keep both (for example, using Cursor’s chat but still liking Copilot’s inline suggestions). If you try this, you might want to disable one’s inline part to avoid overlap. It’s an unusual setup, and support for Copilot inside Cursor might not be guaranteed if Copilot’s extension expects vanilla VS Code (but likely it works). Another alternative: keep VS Code + Copilot open alongside Cursor to compare. However, that’s heavy and most find sticking to one assistant yields a smoother flow. As vibe coding matures, you probably won’t need two – the best features will consolidate. ### **12.3 Getting Help and Staying Supported** If you encounter issues beyond the common ones, don’t hesitate to reach out through the channels above. Cursor’s development team appears to be actively engaging with users (the rapid improvements and bug fixes indicate they listen). They even open-sourced some parts (like their docs, and possibly any SDKs for MCP). The **forum and Discord** (if available) are great for quick help, while **documentation** is your friend for understanding features in depth. Finally, remember that **vibe coding is new for everyone**. You might sometimes be charting unknown waters, encountering an edge-case bug that hasn’t been reported yet. By providing feedback to the Cursor team (with logs or repro steps), you’re not only helping yourself but also contributing to making the tool better for all. It’s a collective journey, much like early days of any developer tech – and being part of that community can be rewarding. Don’t get discouraged by a bump or two; with the tips in this section, you should be able to overcome most issues and get back into that smooth coding groove. ## 13. Conclusion & Next Steps We’ve now taken a comprehensive tour of *“Vibe Coding with Cursor IDE.”* From defining the philosophy of vibe coding and seeing how it emerged, to exploring Cursor’s history and features, diving deep into practical usage, examining case studies, comparing approaches, and troubleshooting – it’s clear that we are standing at the forefront of a new era in software development. **Vibe coding** encourages us to embrace creativity and flow in programming, leveraging AI as a partner to handle the tedious and boilerplate aspects of coding. Instead of viewing code as solely a manual craft, vibe coding frames it as a collaborative art between human and machine. You, the developer, get to focus on ideas, architecture, and solving problems at a high level, while the AI helps fill in the details, accelerates repetitive tasks, and even sparks new ideas. It’s fitting that the term came from the notion of *“surrendering to the flow”* – when you get into a rhythm with Cursor’s AI, coding indeed can feel like a flow state, where things just happen almost as you think of them. **Cursor IDE** has proven to be a capable vehicle for this new style. By integrating a powerful AI directly into a familiar editor, Cursor bridges the gap between traditional coding and AI-assisted development. It brings along all the conveniences of a modern IDE (extensibility, debugging, version control integration) and adds on top an array of AI features: intelligent autocompletion that can predict whole blocks of code, a chat assistant that knows your project and can answer questions or make changes, and an agent that can execute multi-step tasks autonomously. With features like *Model Context Protocol (MCP)*, Cursor is extensible in ways we’ve never seen – it can tie into external tools and effectively act with the wider system on your behalf. This turns the coding environment into something much more: a smart assistant that’s part IDE, part command center. Throughout this e-book, we’ve **learned practical skills**: how to install and set up Cursor, how to get the most out of its AI suggestions, how to tailor your workflow to balance speed and accuracy, how to integrate tools via MCP, and how to troubleshoot common issues. We walked through examples of using vibe coding to build real projects (from a to-do app to updating Jira tickets automatically). If you followed along, you might have even tried some of those exercises in your own Cursor setup. By doing so, you’re not just reading about vibe coding – you’re experiencing it. Now, as we conclude, **what are the next steps for you?** Here are some recommendations: - **1. Apply what you’ve learned:** Take a small project or even a coding challenge and try to implement it with Cursor. Start a fresh repository and let the AI help you from the ground up. Or pick an old project and see if you can improve it (maybe add a feature or refactor some code) using vibe coding techniques. Hands-on practice will cement the concepts and also build your confidence in working with the AI. - **2. Develop your prompting skill:** Make a conscious effort to observe how you communicate with the AI. Over the next week or two of usage, note which prompts yield great results and which don’t. You might even keep a little “prompt journal” – jot down effective phrasing that you can reuse. For instance, you might discover that saying “Provide step-by-step reasoning” before asking a complex question makes the AI’s answer clearer. Becoming a “AI whisperer” is part of becoming a proficient vibe coder. - **3. Engage with the community:** Join the Cursor forum or any community chats. Share your cool use cases or even the hiccups you encounter. By asking questions and seeing how others are using the tool, you’ll accelerate your learning. The landscape of AI coding is evolving rapidly; being plugged into the community ensures you stay updated on new features (like when Cursor releases an update with, say, better memory or a new model integration, you’ll hear about it). You can also share feedback – the developers often incorporate user suggestions quickly. - **4. Keep up with updates and learning resources:** Cursor’s team blog posts about advanced features (for example, the intricacies of Shadow Workspaces or performance improvements). There are also independent content creators (on Medium, YouTube, etc.) making tutorials, demos, and even courses around AI coding tools. Continue to explore those. A year ago, few could imagine coding like this; a year from now, there will be even more to explore (perhaps new AI modes, or more competitors to compare). Staying curious and informed will make you an early expert in a field that is inevitably becoming mainstream. - **5. Integrate vibe coding into your real work carefully:** If you work on professional or team projects, introduce Cursor or vibe coding gradually and responsibly. Maybe start using it for non-critical parts or as a code assistant in read-only mode (ex: ask it to explain code or suggest improvements, which you then implement carefully). Show some success – e.g., “hey team, I used Cursor to reduce our API integration time by 50% for this feature.” That can open minds. At the same time, be mindful of any company policies on code and AI, and use Privacy Mode or self-hosted options if needed. Over time, your team might adopt vibe coding principles officially, which could be a huge productivity win. - **6. Continue to improve non-AI skills too:** Vibe coding doesn’t eliminate the need for solid fundamentals. In fact, the better you are at design, algorithms, and understanding your domain, the better you can direct the AI to implement it. Use the freed-up time from not writing boilerplate to think more about the big picture or to write tests and docs. Essentially, use the AI as a catalyst to become an even better developer. You might find you have more time to learn new frameworks or computer science concepts because the AI took care of some grunt work. In closing, we are at an exciting frontier. The combination of human creativity and AI assistance has opened possibilities to code in a more intuitive and enjoyable way. With Cursor IDE and vibe coding, you have a powerful setup to turn ideas into reality faster than ever before. **Embrace the flow**: brainstorm freely with the AI, iterate rapidly, and don’t fear making mistakes because you can fix them just as fast. The more you collaborate with the AI, the more you’ll get a feel for its strengths and quirks, almost like a colleague. As you continue on this journey, imagine how you might teach someone else – perhaps in the future you’ll be writing an e-book chapter or blog post of your own experiences, or mentoring a new developer who is astonished at how coding “used to be” before AI (you might tell them, *“Yes, we really used to write all those loops by hand!”*). By mastering vibe coding now, you’re positioning yourself at the cutting edge of software development practice. Thank you for reading *“Vibe Coding with Cursor IDE.”* Now it’s time to put these vibes into action. Happy coding, and may you always code with good vibes! **Next Steps:** - Try out Cursor on a new project within the next 24 hours. - Join the community forum and introduce yourself. - Experiment with creating at least one MCP integration that’s relevant to you. - Set a reminder for a month from now to reflect on how vibe coding has changed your workflow – and what you plan to do next with it. Keep the momentum going, stay curious, and enjoy the newfound productivity and creativity that vibe coding brings to your development life. The future is literally at your fingertips (and perhaps just a Tab key away)!